Dr. Sébastien Gérard Dr. Julio Medina Pr. Dorina Petriu · Enhanced version of UML1.x...
117
MARTE: A New Standard for Modeling and Analysis of Real-Time and Embedded Systems Dr. Sébastien Gérard (CEA LIST, France, sebastien.gerard[at]cea.fr) Dr. Julio Medina (CEA LIST, France, julio.medina[at]cea.fr) Pr. Dorina Petriu (Carleton University, Canada, petriu[at]sce.carleton.ca) (with the contributions of: Huascar Espinoza, Frédéric Thomas and Safouan Taha) / MARTE Tutorial 2 Agenda 09:00 am to 10:15 am Introduction to MDD for RT/E systems MARTE foundations 10:15 am to 10:45 am: Break 10:45 am to 12:00 am High-level modeling constructs Detailed software and hardware platforms modeling 13:00 pm to 14:30 pm: Lunch 14:30 pm to 15:00 pm Introduction to model-based RTE analysis 15:00 pm to 15:45 pm Model-based schedulability analysis 15:45 pm to 16:15 pm: Break 16:15 pm to 17:00 pm Model-based performance analysis 17:00 pm to 17:30 pm Conclusions and perspectives about MARTE
Transcript of Dr. Sébastien Gérard Dr. Julio Medina Pr. Dorina Petriu · Enhanced version of UML1.x...
MARTE:A New Standard for Modeling and Analysisof Real-Time and Embedded Systems
Dr. Sébastien Gérard(CEA LIST, France, sebastien.gerard[at]cea.fr)
Dr. Julio Medina(CEA LIST, France, julio.medina[at]cea.fr)
Pr. Dorina Petriu(Carleton University, Canada, petriu[at]sce.carleton.ca)
(with the contributions of: Huascar Espinoza, Frédéric Thomas and Safouan Taha)
/ MARTE Tutorial 2
Agenda09:00 am to 10:15 am
� Introduction to MDD for RT/E systems� MARTE foundations
10:15 am to 10:45 am: Break 10:45 am to 12:00 am
� High-level modeling constructs� Detailed software and hardware platforms modeling
13:00 pm to 14:30 pm: Lunch 14:30 pm to 15:00 pm
� Introduction to model-based RTE analysis 15:00 pm to 15:45 pm
� Model-based schedulability analysis 15:45 pm to 16:15 pm: Break 16:15 pm to 17:00 pm
� Model-based performance analysis 17:00 pm to 17:30 pm
� Conclusions and perspectives about MARTE
/ MAR
TE T
utor
ial
3
Mod
els
in T
radi
tiona
l Eng
inee
ring
�P
roba
bly
as o
ld a
s en
gine
erin
g
Extra
cted
from
B. S
elic
pre
sent
atio
n du
ring
Sum
mer
Sch
ool
MD
D F
or D
RES
200
4 (B
rest
, Sep
tem
ber 2
004)
/ MAR
TE T
utor
ial
4
Wha
t is
a M
odel
in M
DD
�O
ne d
efin
ition
�A
redu
ced/
abst
ract
repr
esen
tatio
n of
som
e sy
stem
that
hig
hlig
hts
the
prop
ertie
s of
inte
rest
from
a g
iven
vie
wpo
int.
�Th
e vi
ewpo
int d
efin
es c
once
rn, s
cope
and
det
ail l
evel
of t
he m
odel
.
Func
tiona
l mod
elMo
deled
syst
em
Insp
ired
from
B. S
elic
pre
sent
atio
n du
ring
Sum
mer
Sch
ool
MD
D F
or D
RES
200
4 (B
rest
, Sep
tem
ber 2
004)
/ MAR
TE T
utor
ial
5
Why
Mod
el D
riven
Eng
inee
ring
is N
eede
d?�
To d
eal w
ith c
ompl
exity
of s
yste
ms
deve
lopm
ent
�A
bstra
ct a
pro
blem
to fo
cus
on s
ome
parti
cula
r poi
nts
of in
tere
st�
impr
ove
unde
rsta
ndab
ility
of a
pro
blem
�P
ossi
ble
set o
f nea
rly in
depe
nden
t vie
ws
of a
mod
el�
Sep
arat
ion
of c
once
rns
(e.g
.“A
spec
t Orie
nted
Mod
elin
g”)
�Ite
rativ
e m
odel
ing
may
be
expr
esse
d at
diff
eren
t lev
el o
f fid
elity
�To
min
imiz
e de
velo
pmen
t ris
ks�
Thro
ugh
anal
ysis
and
exp
erim
enta
tion
perfo
rmed
ear
lier i
n th
e de
sign
cyc
le
�E
nabl
e to
inve
stig
ate
and
com
pare
alte
rnat
ive
solu
tions
�To
impr
ove
com
mun
icat
ion
......
to fo
ster
info
rmat
ion
shar
ing
and
reus
e!�
A m
odel
is o
ften
best
sui
ted
than
a lo
ng s
peec
h !
�To
focu
son
spe
cific
dom
ain
expe
rtise
whi
le d
evel
opin
g so
ftwar
e sy
stem
�D
omai
n S
peci
fic L
angu
age
/ MAR
TE T
utor
ial
6
Cha
ract
eris
tics
of U
sefu
l Mod
els
�A
bstra
ct�
Em
phas
ize
impo
rtant
asp
ects
whi
le re
mov
ing
irrel
evan
t one
s�
Und
erst
anda
ble
�E
xpre
ssed
in a
form
that
is re
adily
und
erst
ood
by o
bser
vers
�A
ccur
ate
�Fa
ithfu
lly re
pres
ents
the
mod
eled
sys
tem
�P
redi
ctiv
e�
Can
be
used
to a
nsw
er q
uest
ions
abo
ut th
e m
odel
ed s
yste
m�
Inex
pens
ive
�M
uch
chea
per t
o co
nstru
ct a
nd s
tudy
than
the
mod
eled
sys
tem
To b
e us
eful,
eng
ineer
ing m
odels
mus
t sat
isfy a
ll of
thes
e ch
arac
teris
tics!
To b
e us
eful,
eng
ineer
ing m
odels
mus
t sat
isfy a
ll of
thes
e ch
arac
teris
tics! (E
xtra
cted
from
B. S
elic
pre
sent
atio
ndu
ring
Sum
mer
Sch
oolM
DD
For
DR
ES
200
4 (B
rest
, Sep
tem
ber2
004)
/ MAR
TE T
utor
ial
7
SC_M
OD
ULE
(pro
duce
r){ sc
_out
mas
ter<
int>
out
1;sc
_in<
bool
> st
art;
// ki
ck-s
tart
void
gen
erat
e_da
ta ()
{ for(
int i
=0;
i <1
0; i+
+) {
out1
=i ;
//to
invo
ke s
lave
;}} SC
_CTO
R(p
rodu
cer)
{ SC_M
ETH
OD
(gen
erat
e_da
ta);
sens
itive
<<
star
t;}};
SC_M
OD
ULE
(con
sum
er)
{ sc_i
nsla
ve<i
nt>
in1;
int s
um; /
/ sta
te v
aria
ble
void
acc
umul
ate
(){su
m +
= in
1;co
ut <
< “S
um =
“ <
< su
m <
< en
dl;}
SC_C
TOR
(con
sum
er)
{ SC_S
LAVE
(acc
umul
ate,
in1)
;su
m =
0; /
/ ini
tializ
e }; SC
_MO
DU
LE(to
p) //
con
tain
er{ pr
oduc
er *A
1;co
nsum
er *B
1;sc
_lin
k_m
p<in
t> li
nk1;
SC_C
TOR
(top)
{ A1
= ne
w p
rodu
cer(
“A1”
);A
1.ou
t1(li
nk1)
;B
1 =
new
con
sum
er(“
B1”
);B
1.in
1(lin
k1);}
};
A B
it of
Mod
ern
Sof
twar
e…
Can
you
spot
the a
rchi
tect
ure?
Can
you
spot
the a
rchi
tect
ure?
(Ext
ract
edfro
mB
. Sel
ic p
rese
ntat
ion
durin
gS
umm
erS
choo
lMD
D F
or D
RE
S 2
004
(Bre
st, S
epte
mbe
r200
4)
/ MAR
TE T
utor
ial
8
…an
d its
UM
L M
odel
«sc_
ctor»
cons
umer
«« sc_
ctor
sc_c
tor»»
cons
umer
cons
umer
«sc_
ctor»
prod
ucer
«« sc_
ctor
sc_c
tor»»
prod
ucer
prod
ucer
star
t
«sc_
link_
mp»
link1
Can
you
spot
the a
rchi
tect
ure?
Can
you
spot
the a
rchi
tect
ure?
(Ext
ract
edfro
mB
. Sel
ic p
rese
ntat
ion
durin
gS
umm
erS
choo
lMD
D F
or D
RE
S 2
004
(Bre
st, S
epte
mbe
r200
4)
/ MAR
TE T
utor
ial
9
refin
e
NotS
tarte
d
Star
tedstar
t
prod
ucer
Mod
el E
volu
tion:
Ref
inem
ent
�M
odel
s ca
n be
refin
ed c
ontin
uous
ly u
ntil
the
spec
ifica
tion
is c
ompl
ete
«sc_
met
hod»
prod
ucer
«sc_
met
hod»
prod
ucer
star
tou
t1
NotS
tarte
d
Star
tedstar
t
prod
ucer
St1
St2
void
gen
erat
e_da
ta()
{for (
int i
=0; i
<10;
i++)
{o
ut1 =
i;}}
/gen
erat
e_da
ta( )
(Ext
ract
edfro
mB
. Sel
ic p
rese
ntat
ion
durin
gS
umm
erS
choo
lMD
D F
or D
RE
S 2
004
(Bre
st, S
epte
mbe
r200
4)
/ MAR
TE T
utor
ial
10
Mod
el-D
riven
Sty
le o
f Dev
elop
men
t (M
DD
)
�A
n ap
proa
ch to
sof
twar
e de
velo
pmen
t in
whi
ch th
e fo
cus
and
prim
ary
artif
acts
of d
evel
opm
ent a
re m
odel
s (a
s op
pose
d to
pr
ogra
ms)
�B
ased
on
two
time-
prov
en m
etho
ds
SC_M
OD
ULE
(pro
duce
r){s
c_in
slav
e<in
t> in
1;in
t sum
; //
void
acc
umul
ate
(){su
m +
= in
1;co
ut <
< “S
um =
“ <
< su
m <
< en
dl;}
«« sc_
mod
ule
sc_m
odul
e »»pr
oduc
erpr
oduc
erst
art
out1
(1) A
BST
RA
CTI
ON
(2) A
UTO
MA
TIO
N
«« sc_
mod
ule
sc_m
odul
e »»pr
oduc
erpr
oduc
erst
art
out1
SC_M
OD
ULE
(pro
duce
r){s
c_in
slav
e<in
t> in
1;in
t sum
; //
void
acc
umul
ate
(){su
m +
= in
1;co
ut <
< “S
um =
“ <
< su
m <
< en
dl;}
Realm
of
mod
eling
langu
ages
Realm
of t
ools
(Ext
ract
edfro
mB
. Sel
ic p
rese
ntat
ion
durin
gS
umm
erS
choo
lMD
D F
or D
RE
S 2
004
(Bre
st, S
epte
mbe
r200
4)
/ MAR
TE T
utor
ial
11
The
Rem
arka
ble
Thin
g A
bout
Sof
twar
e
Sof
twar
e ha
s th
e ra
re p
rope
rty t
hat
it al
low
s us
to
di
rect
ly
evol
ve
mod
els
into
fu
lly-fl
edge
d im
plem
enta
tions
w
ithou
t ch
angi
ng
the
engi
neer
ing
med
ium
, too
ls, o
r met
hods
!
�Th
is en
sure
s per
fect a
ccur
acy o
f soft
ware
mod
els,
since
the m
odel
and t
he sy
stem
that it
mod
els ar
e the
same
thing
.Th
e so
ftwar
e m
odel
may
evo
lve in
to th
e sy
stem
it wa
s m
odeli
ng in
a se
amles
s pro
cess
.(E
xtra
cted
from
B. S
elic
pre
sent
atio
ndu
ring
Sum
mer
Sch
oolM
DD
For
DR
ES
200
4 (B
rest
, Sep
tem
ber2
004)
/ MAR
TE T
utor
ial
12
Two
Kin
ds o
f OM
G M
odel
ing
Lang
uage
s�
Gen
eral
Pur
pose
Lang
uage
(GP
L)�
E.g
., U
ML2
, Sys
ML
and
CC
M�
Dom
ain
Spe
cific
Lang
uage
�E
.g. L
wC
CM
, UM
L pr
ofile
for S
oCan
d M
AR
TE
M2
Leve
l(L
angu
age
Mod
el L
evel
)
GPL
(Gen
eral
Pur
pose
Lan
guag
e)
DSL
(Dom
ain
Spec
ific
Lang
uage
)
«pr
ofile
»U
ML
prof
ile fo
r SoC
«pr
ofile
»U
ML
prof
ile M
AR
TE
«re
fere
nce
»«
refe
renc
e»
/ MAR
TE T
utor
ial
13
From
UM
L 1.
x to
UM
L 2.
0: S
tatic
Poi
nt o
f Vie
w
�C
ompo
nent
ava
ilabl
e at
des
ign-
time
not o
nly
at im
plem
enta
tion-
time.
�M
odifi
ed to
sup
port
new
com
pone
nt
conc
ept d
efin
ition
.
�H
iera
rchi
cal s
truct
ure
desc
riptio
n of
cl
assi
fiers
: Por
ts a
nd P
arts
.
/ MAR
TE T
utor
ial
14
From
UM
L 1.
x to
UM
L 2.
0: D
ynam
icP
oint
of V
iew
UM
L2 B
ehav
ior D
iagr
am
Stat
e M
achi
ne D
iagr
amA
ctiv
ity D
iagr
amU
se C
ase
Dia
gram
New
Mod
ified
Sequ
ence
Dia
gram
Com
mun
icat
ion
Dia
gram
Tim
ing
Dia
gram
Inte
ract
ion
Ove
rvie
w D
iagr
amN
ewN
ewDep
ict d
ata/
obje
ct/c
ontro
l flo
w d
iagr
ams:
�D
efin
ed in
depe
nden
tly o
f sta
tem
achi
nes
�P
etri-
net l
ike
sem
antic
s
Enh
ance
d ve
rsio
n of
UM
L1.x
sta
tem
achi
ne:
�S
ome
new
con
cept
s an
d no
tatio
n sh
ortc
uts
(e.g
. Ent
ryP
oint
, E
xitP
oint
in c
ompo
site
sta
te)
�P
roto
col s
tate
mac
hine
s
Focu
s on
sys
tem
sta
te c
hang
es re
gard
ing
time
prog
ress
ion.
�no
t ver
y cl
early
def
ined
! Spe
cial
izat
ion
of a
ctiv
ity d
iagr
ams
�A
ctiv
itygr
aph
of S
D o
r CD
.�
~ to
Hig
h-le
vel M
essa
ge S
eque
nce
Cha
rt of
SD
L
Pre
viou
s “C
olla
bora
tion
diag
ram
s”of
UM
L1.x
/ MAR
TE T
utor
ial
15
Sys
ML:
A U
ML
Ext
ensi
on fo
r Sys
tem
Eng
inee
ring
�S
yste
ms
Mod
elin
g La
ngua
ge (w
ww
.om
gsys
ml.o
rg)
�G
ener
al-p
urpo
se s
yste
ms
mod
elin
g la
ngua
ge�
Spe
cific
atio
n, a
naly
sis,
des
ign,
ver
ifica
tion
and
valid
atio
n of
a b
road
rang
e of
co
mpl
ex s
yste
ms
�E
xpec
ted
to b
e cu
stom
ized
to m
odel
dom
ain
spec
ific
appl
icat
ions
�E
.g.,
Spa
ce, A
utom
otiv
e, A
eros
pace
and
Com
mun
icat
ions
�U
ML-
com
patib
le s
yste
ms
mod
elin
g la
ngua
ge�
For s
uppo
rting
the
exch
ange
of i
nfor
mat
ion
usin
g st
anda
rdiz
ed n
otat
ions
an
d se
man
tics
that
are
und
erst
ood
in p
reci
se a
nd c
onsi
sten
t way
s
�A
ligne
d w
ith th
e IS
O A
P-2
33 s
tand
ard
/ MAR
TE T
utor
ial
16
From
UM
L 2.
0 to
Sys
ML:
Sta
ticP
oint
of V
iew
Min
or e
xten
sion
s:�
Sho
rtcut
for i
nher
itanc
e m
odel
ing.
�C
once
pts
of d
epen
denc
y gr
oup.
�R
euse
d of
UM
L2 s
truct
ured
cla
sses
.�
Pro
vide
d m
ore
neut
ral c
once
pt fo
r an
y ki
nd o
f dom
ain
(not
onl
y pr
epon
dera
nt s
oftw
are
syst
ems)
.
�D
efin
e gr
aphi
cal n
otat
ions
for
mat
hem
atic
al o
r log
ical
con
stra
ints
.
/ MAR
TE T
utor
ial
17
From
UM
L 2.
0 to
Sys
ML:
Dyn
amic
Poi
nt o
f Vie
w
UM
L2 B
ehav
ior D
iagr
am
Stat
e M
achi
ne D
iagr
amA
ctiv
ity D
iagr
amU
se C
ase
Dia
gram
New
Mod
ified
Sequ
ence
Dia
gram
Com
mun
icat
ion
Dia
gram
Tim
ing
Dia
gram
Inte
ract
ion
Ove
rvie
w D
iagr
amN
ewN
ew
Ext
ensi
ons
of S
ysM
L ac
tivity
dia
gram
:�
New
type
s of
obj
ect/d
ata
flow
(c
ontin
uous
, stre
amin
g or
rate
.)�
Pro
babi
lity
on e
dges
of t
he g
raph
.
/ MAR
TE T
utor
ial
18
Dom
ain
Spe
cific
Mod
elin
g La
ngua
ges
�U
se o
f dom
ain
conc
epts
as
lang
uage
con
stru
cts
�E
.g. c
once
pts
of “m
emor
y”, “
task
”, “b
us”,
inst
ead
of g
ener
al o
nes
“com
pone
nts”
, “no
des”
, “cl
asse
s”…
�C
riter
ia fo
r suc
cess
of a
DS
ML
�Te
chni
cal v
alid
ity a
nd a
bsen
ce o
f am
bigu
ities
(MoC
Cs
map
ping
)�
Inte
rope
rabi
lity
with
oth
er te
chno
logi
es (v
erifi
catio
n, c
ode
gene
ratio
n)�
Dom
ain
appr
opria
tene
ss: s
ynta
x cl
ose
to e
mbe
dded
sys
tem
s on
tolo
gy�
Sup
port:
tool
s, d
ocum
enta
tion,
cap
acity
for e
volu
tion
�M
ain
appr
oach
es to
def
inin
g a
DS
ML
�D
efin
e a
new
lang
uage
from
scr
atch
�E
xten
d an
exi
stin
g la
ngua
ge (a
dd n
ew la
ngua
ge c
onst
ruct
s)�
Ref
ine
an e
xist
ing
lang
uage
(spe
cial
ize
lang
uage
con
stru
cts)
But
mai
nly…
bala
nce
betw
een
expr
essi
ve p
ower
and
sim
plic
ity!
/ MAR
TE T
utor
ial
19
Whi
chTe
chni
csfo
r Mak
ing
an O
MG
DS
L?�
Met
a-m
odel
s (u
sing
MO
F)�
For h
eavy
wei
ght e
xten
sion
mec
hani
sms
�E
nsur
es fu
ll m
anip
ulat
ion
of M
Ms
�A
dd, r
emov
e m
eta-
clas
ses
and
rela
tions
hips
inbe
twee
n�
Pro
files
�Fo
r lig
htw
eigh
t ext
ensi
on m
echa
nism
s�
Ada
ptat
ion
of e
xist
ing
met
a-m
odel
s w
ithou
t mod
ifica
tions
!�
E.g
., O
nly
poss
ible
to a
dd c
onst
rain
ts�
May
ext
end
any
stan
dard
MM
of t
he O
MG
�E
.g.,
UM
L pr
ofile
s an
d C
WM
pro
files
�P
rofil
es v
s. m
eta-
mod
els
�It
may
dep
end
on:
�S
cope
of t
he e
xten
sion
s�
Tool
ing
cons
train
ts�
Eng
inee
r lev
el o
f exp
erim
ent/e
duca
tion
�…
Ext
ract
edfro
mU
ML
supe
rstru
ctur
e do
c.:
"The
re is
no
sim
ple
answ
er fo
r whe
n yo
u sh
ould
cre
ate
a ne
w m
etam
odel
and
w
hen
you
inst
ead
shou
ld c
reat
e a
new
prof
ile."
/ MAR
TE T
utor
ial
20
Pro
filin
g U
ML
for a
Dom
ain
�A
dvan
tage
s of
UM
L P
rofil
es�
Reu
se o
f lan
guag
e in
frast
ruct
ure
(tool
s, s
peci
ficat
ions
)�
Req
uire
less
lang
uage
des
ign
skill
s�
Allo
w fo
r new
(gra
phic
al) n
otat
ion
of e
xten
ded
ster
eoty
pes
�A
pro
file
can
defin
e m
odel
vie
wpo
ints
�E
.g.,
UM
L ac
tivity
dia
gram
ext
ende
d to
spe
cify
mul
titas
k be
havi
or
�D
isad
vant
age
�C
onst
rain
ed b
y U
ML
met
amod
el
�H
ow to
face
thes
e lim
itatio
ns?
�P
rofil
e se
man
tics:
on-
goin
g w
ork:
‘Exe
cuta
ble
UM
L Fo
unda
tion’
�C
lear
map
ping
of p
rofil
e ex
tens
ions
to d
omai
n on
tolo
gy�
Cre
ate
rich
prof
iles
(exp
ress
iven
ess)
and
com
plem
ent w
ith s
peci
fic
met
hodo
logi
es (p
reci
se s
eman
tics
and
prag
mat
ics)
/ MAR
TE T
utor
ial
21
Mai
n G
oals
for M
akin
g U
ML
Pro
files
�D
efin
e a
dom
ain
spec
ific
term
inol
ogy
�D
efin
e a
dom
ain
spec
ific
nota
tion
�D
efin
e co
ncre
te s
ynta
x to
spe
cific
ele
men
ts�
E.g
., fo
r act
ion
�D
efin
e se
man
tics
rule
for:
�V
aria
tion
poin
ts�
E.g
., O
n B
road
cast
Sig
nalA
ctio
n, s
cope
of t
arge
t obj
ects
in S
igna
l.�
Am
bigu
ous
defin
ition
�N
ew p
urpo
use
�A
dd u
sage
con
stra
ints
on
the
UM
L M
M�
e.g.
for m
etho
d po
int o
f vie
w�
E.g
., to
forc
e be
havi
or s
peci
ficat
ion
via
prot
ocol
sta
te-m
achi
ne�
Tag
a m
odel
with
add
ition
al in
form
atio
n�
e.g.
for c
ode
gene
ratio
n pu
rpos
e�
E.g
., fo
r C++
, «vi
rtual
», «
inlin
e»…
/ MAR
TE T
utor
ial
22
Out
lines
of U
ML2
Ext
ensi
on M
echa
nism
s
�P
rofil
es�
Def
ine
limite
d ex
tens
ions
to a
refe
renc
e m
etam
odel
with
the
purp
ose
of
adap
ting
the
met
amod
elto
a s
peci
fic p
latfo
rm o
r dom
ain.
�C
onsi
sts
of s
tere
otyp
es e
xten
ded
the
met
amod
elcl
asse
s.�
Ste
reot
ypes
�D
efin
e ho
w a
spe
cific
met
acla
ssm
ay b
e ex
tend
ed�
Pro
vide
add
ition
al s
eman
tics
info
rmat
ion,
but
onl
y fo
r:�
Sem
antic
s re
stric
tion
or c
larif
icat
ion
of e
xist
ing
conc
ept
�N
ew fe
atur
es (b
ut c
ompa
tible
with
exi
ting
one!
)�
Ens
ure
intro
duct
ion
of d
omai
n sp
ecifi
c te
rmin
olog
y�
E.g
., E
AS
T-A
DL2
, a U
ML
prof
ile fo
r aut
omot
ive
EC
Us
(http
://w
ww
.ate
sst.o
rg)
�M
ay d
efin
e sp
ecifi
c no
tatio
n�
E.g
., ne
w ic
ons
or s
hape
s
�M
ay h
ave
valu
es th
at a
re u
sual
ly re
ffere
dto
as
tagg
ed v
alue
s
/ MAR
TE T
utor
ial
27
yes
OM
G S
tand
ardi
zatio
nP
roce
ss
RFP
FTF
?ye
s
no
Pro
p-1
…P
rop-
nP
rop R
evis
ed V
ersi
on
RFI
WP
?no
yes
?no
?ye
s
no
MA
RTE
v1.
0
FTF
Dra
ftA
dopt
edS
peci
ficat
ion:
16
July
200
7
MA
RTE
FTF
: Com
men
ts d
ue fo
r 22
dece
mbe
r 200
7 !
Fina
l Ado
pted
Spe
cific
atio
nP
ublic
atio
n: 6
Aug
ust 2
007
Com
men
ts D
ue: 2
2 D
ecem
ber 2
007
Rec
omm
enda
tions
and
Rep
ort D
eadl
ine:
4 J
uly
2008
/ MAR
TE T
utor
ial
28
UM
L P
rofil
es fo
r RTE
S
�S
PT
was
the
first
OM
G’s
UM
L pr
ofile
for R
eal-T
ime
Sys
tem
s:�
Sup
port
for S
ched
ulab
ility
Anal
ysis
with
RM
A-ty
pe te
chni
ques
�S
uppo
rt fo
r Per
form
ance
Ana
lysi
s w
ith Q
ueui
ng T
heor
y an
d P
etri
Net
s�
A ri
ch m
odel
for “
met
ric”T
ime
and
Tim
e M
echa
nism
s
�D
espi
te it
s ex
tens
ive
use,
sev
eral
impr
ovem
ents
wer
e re
quire
d:�
Mod
elin
g H
W a
nd S
W p
latfo
rms,
Log
ical
Tim
e, M
oCC
s, C
BS
E…
�A
lignm
ent t
o U
ML2
, QoS
&FT
, MD
A,…
�S
PT’
sco
nstru
cts
wer
e co
nsid
ered
too
abst
ract
and
har
d to
app
ly�
…
SPT
(200
5-ad
opte
d)
Hen
ce, a
Req
uest
For
Pro
posa
l for
a n
ew p
rofil
e w
as is
sued
.
/ MAR
TE T
utor
ial
29
Gen
eral
Req
uire
men
ts o
f the
MA
RTE
RFP
�P
ropo
sals
sha
ll su
ppor
t Mod
elin
g an
d A
naly
sis
of R
eal-T
ime
and
Em
bedd
ed (i
n sh
ort M
AR
TE) s
yste
ms
incl
udin
g its
so
ftwar
e an
d ha
rdw
are
aspe
cts.
�
The
Pro
posa
ls w
ill d
efin
e a
met
amod
el a
nd it
s un
derly
ing
UM
L pr
ofile
. �
It sh
all b
e po
ssib
le to
use
inde
pend
ently
sof
twar
e an
d ha
rdw
are
parts
of t
he p
rofil
e.
�It
shal
l com
ply
with
exi
stin
g st
anda
rds
�It
shal
l upd
ate
the
SP
T pr
ofile
1.1
Ref
. of M
AR
TE R
FP (r
ealti
me/
05-0
2-06
):ht
tp://
ww
w.o
mg.
org/
cgi-b
in/d
oc?r
ealti
me/
05-0
2-06 / M
ARTE
Tut
oria
l30
The
Pro
MA
RTE
Team
* S
ubm
itter
to O
MG
UM
L P
rofil
e fo
r MA
RTE
RFP
�P
ublic
web
site
=>
ww
w.p
rom
arte
.org
�P
artn
ers
�In
dust
rials
�A
lcat
el*
�Lo
ckhe
ed M
artin
*�
Thal
es*
�To
olve
ndor
s�
AR
TIS
AN
Sof
twar
e To
ols*
�In
tern
atio
nal B
usin
ess
Mac
hine
s*�
Men
tor G
raph
ics
Cor
pora
tion*
�S
ofte
am*
�Te
lelo
gic
AB (I
-Log
ix*)
�Tr
i-Pac
ific
Sof
twar
e�
Fran
ce T
elec
om�
No
Mag
ic�
Mat
hwor
ks�
Aca
dem
ics
�C
arle
ton
Uni
vers
ity�
Com
mis
saria
t àl’E
nerg
ieAt
omiq
ue�
ESEO
�E
NS
IETA
�IN
RIA
�IN
SA
from
Lyo
n�
Softw
are
Engi
neer
ing
Inst
itute
(Car
negi
e M
ello
n U
nive
rsity
)�
Uni
vers
idad
de
Can
tabr
ia
/ MAR
TE T
utor
ial
31
Rel
atio
nshi
ps w
ith o
ther
OM
G S
tand
ards
�R
elat
ions
hips
with
gen
eric
OM
G s
tand
ards
�P
rofil
e th
e U
ML2
sup
erst
ruct
ure
met
a-m
odel
�R
epla
ce U
ML
prof
ile fo
r SP
T�
Use
OC
L2�
Rel
atio
nshi
ps w
ith R
T&E
spe
cific
OM
G s
tand
ards
�E
xist
ing
stan
dard
s�
The
UM
L pr
ofile
for M
odel
ing
QoS
and
FT
Cha
ract
eris
tics
and
Mec
hani
sms
�A
ddre
ssed
thro
ugh
MA
RTE
NFP
pac
kage
(in
a w
ay d
etai
led
in th
e N
FP p
rese
ntat
ion)
�
The
UM
L pr
ofile
for S
oC�
Mor
e sp
ecifi
c th
an M
AR
TE p
urpo
se�
The
Rea
l-Tim
e C
OR
BA
pro
file
�R
eal-T
ime
CO
RB
A b
ased
arc
hite
ctur
e ca
n be
ann
otat
ed fo
r ana
lysi
s w
ithM
arte
�Th
e U
ML
prof
ile fo
r Sys
tem
s E
ngin
eerin
g�
Spe
cial
izat
ion
of S
ysM
Lal
loca
tion
conc
epts
and
reus
e of
flow
-rela
ted
conc
epts
�O
ngoi
ng d
iscu
ssio
n to
incl
ude
VS
L in
nex
t Sys
ML
vers
ion
�O
verla
p of
team
mem
bers
/ MAR
TE T
utor
ial
32
Des
ign
Pat
tern
Ado
pted
for t
he M
AR
TE P
rofil
e�
Sta
ge 1�
Des
crip
tion
of M
AR
TE d
omai
n m
odel
s�
Pur
pose
: Fo
rmal
des
crip
tion
of th
e co
ncep
ts re
quire
d fo
r MA
RTE
�Te
chni
ques
: M
eta-
mod
elin
g
�S
tage
2�
Map
ping
of M
AR
TE d
omai
n m
odel
s to
war
ds U
ML2
�P
urpo
se:
MA
RTE
dom
ain
mod
els
desi
gn a
s a
UM
L2 e
xten
sion
s�
Tech
niqu
es:
UM
L2 p
rofil
e
«m
etam
odel
»M
arte
dom
ain
mod
el
«pr
ofile
»M
arte
pro
file
UM
L2 m
etam
odel
«re
fere
nce
»
«re
fine
»
/ MAR
TE T
utor
ial
33
MA
RTE
Ove
rvie
wFo
unda
tions
for R
T/E
sys
tem
sm
odel
ing
and
anal
ysis
:�
Cor
eEle
men
ts�
NFP
s�
Tim
e�
Gen
eric
reso
urce
mod
elin
g�
Gen
eric
com
pone
nt m
odel
ing
�Al
loca
tion
Spe
cial
izat
ion
of fo
unda
tions
for
anno
tatin
g m
odel
for a
naly
sis
purp
ose:
�
Gen
eric
qua
ntita
tive
anal
ysis
�Sc
hedu
labi
lity
anal
ysis
�
Per
form
ance
ana
lysi
s
Spec
ializ
atio
n of
MAR
TE fo
ndat
ions
for m
odel
ing
purp
ose
(spe
cific
atio
n,
desi
gn…
): �
RTE
mod
el o
f com
puta
tion
and
com
mun
icat
ion
�S
oftw
are
reso
urce
mod
elin
g�
Har
dwar
e re
sour
ce m
odel
ing
/ MAR
TE T
utor
ial
34
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT&
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
35
Out
lines
of t
he M
AR
TE F
ound
atio
ns
�M
AR
TE fo
unda
tions
�D
efin
e a
set o
f bas
ic c
once
pts
for M
DD
of R
TES
�In
tend
ed to
be
used
as
basi
c la
yer a
t OM
G fo
r fut
ure
RTE
rela
ted
stds
�C
onsi
sts
of fi
ve s
ub-p
rofil
es
/ MAR
TE T
utor
ial
36
Non
-Fun
ctio
nal P
rope
rties
(NFP
s)
�N
atur
e of
NFP
s�
Qua
ntita
tive:
mag
nitu
de +
uni
t (E
.g.,
ener
gy, d
ata
size
and
dur
atio
n)�
Qua
litat
ive
(E.g
., pe
riodi
c or
spo
radi
c ev
ent a
rriv
al p
atte
rns)
�N
FP v
alue
s ne
ed to
be
qual
ified
�E
.g. s
ourc
e, s
tatis
tical
mea
sure
, pre
cisi
on,…
�N
FPs
need
to b
e pa
ram
etric
and
der
ivab
le�
Var
iabl
es: p
lace
hold
ers
for u
nkno
wn
valu
es�
Exp
ress
ions
: mat
h. a
nd ti
me
expr
essi
ons
�N
FPs
need
cle
ar s
eman
tics
�P
rede
fined
NFP
s(E
.g.,
end-
to-e
nd la
tenc
y, p
roce
ssor
util
izat
ion)
�U
ser-
spec
ific
NFP
s(b
ut s
till u
nam
bigu
ousl
y in
terp
rete
d!)
Non
-func
tiona
l pro
perti
es d
escr
ibe
the
“fitn
ess”
of s
yste
ms
beha
vior
(E.g
., pe
rform
ance
, mem
ory
usag
e an
d po
wer
con
sum
ptio
n)
/ MAR
TE T
utor
ial
37
Intro
duct
ion
to th
e M
AR
TE’s
NFP
sFr
amew
ork
Stru
ctur
ed V
alue
s?V
aria
bles
?
Com
plex
tim
e ex
pres
sion
s?D
ata
Type
Sys
tem
?
Val
ue q
ualif
iers
?
UM
L la
cks
mod
elin
g ca
pabi
litie
s fo
r NFP
s!!
NFP
Lib
rarie
s?
Mea
sure
s?
UM
L P
rofil
e fo
r NFP
s
Val
ue S
peci
ficat
ion
Lang
uage
(VS
L)
But
UM
L ex
pres
sion
syn
tax
is a
lso
not s
uffic
ient
!!
Ann
otat
ion
mec
hani
sm?
/ MAR
TE T
utor
ial
38
The
MA
RTE
’sN
FP s
ub-p
rofil
e
�R
elie
s on
the
abili
ty to
put
add
ition
al in
form
atio
n on
mod
els
�Th
ree
poss
ible
mod
el-b
ased
ann
otat
ion
mec
hani
sms
in U
ML
�V
alue
of s
tere
otyp
e pr
oper
ties
�S
lots
val
ue o
f cla
ssifi
er in
stan
ce
�C
onst
rain
ts
/ MAR
TE T
utor
ial
39
Ann
otat
ing
NFP
sin
Tag
ged
Val
ues
2) D
efin
e N
FP-li
ke e
xten
sion
s
1) D
ecla
re N
FP ty
pes
3) S
peci
fy N
FP v
alue
s
�D
efin
e st
ereo
type
s an
d th
eir
attri
bute
s us
ing
NFP
type
s
�A
pply
ste
reot
ypes
and
spe
cify
th
eir a
ttrib
ute
valu
es u
sing
VS
L
�D
efin
e m
easu
rem
ent u
nits
and
co
nver
sion
par
amet
ers
�D
efin
e N
FP ty
pes
with
qua
lifie
rs
resp
T: N
FP_D
urat
ion
...
«ste
reot
ype»
Scen
ario
exam
ple
resp
T: N
FP_D
urat
io...
«ste
reot
ype»
Scen
ario
exxaamm
plle
MA
RTE
pre
-def
ined
/ MAR
TE T
utor
ial
40
Ann
otat
ing
NFP
sin
Slo
ts
2) D
ecla
re N
FPs
1) D
ecla
re N
FP ty
pes
3) S
peci
fy N
FP v
alue
s
�D
efin
e cl
assi
fiers
and
thei
r at
tribu
tes
usin
g N
FP ty
pes
�S
uch
attri
bute
s ar
e m
arke
d as
«nf
p»
�In
stan
tiate
cla
ssifi
ers
and
spec
ify th
eir s
lot v
alue
s us
ing
VSL
�D
efin
e m
easu
rem
ent u
nits
and
co
nver
sion
par
amet
ers
�D
efin
e N
FP ty
pes
with
qua
lifie
rs
«im
port
»
Cla
ssifi
erM
odel
«nfp
» de
adlin
e: N
FP_D
urat
ion
...
Task
exam
ple
rt»
Cla
ssifi
erM
odel
«nfp
» de
adlin
e: N
FP_D
urat
ion
...
Task
exxaamm
plle
Mod
el-s
peci
fic N
FPs
/ MAR
TE T
utor
ial
41
Ann
otat
ing
NFP
sin
Con
stra
ints
2) D
ecla
re N
FPs
1) D
ecla
re N
FP ty
pes
3) S
peci
fy N
FP v
alue
s
�D
efin
e cl
assi
fiers
and
thei
r at
tribu
tes
usin
g N
FP ty
pes
�C
reat
e C
onst
rain
ts to
def
ine
asse
rtion
s on
NFP
val
ues
usin
g V
SL
�D
efin
e m
easu
rem
ent u
nits
and
co
nver
sion
par
amet
ers
�D
efin
e N
FP ty
pes
with
qua
lifie
rs
«im
port
»
Cla
ssifi
erM
odel
«nfp
» ut
iliza
tion:
NFP
_Per
cent
age
«nfp
» cl
ockF
req:
NFP
_Fre
quen
cy...
Proc
esso
r
exam
ple
/ MAR
TE T
utor
ial
42
The
MA
RTE
’sN
FP M
odel
ing
Fram
ewor
k
Val
ue S
peci
ficat
ion
Lang
uage
(VS
L)
Thre
e m
ain
lang
uage
ext
ensi
ons
to U
ML
synt
ax:
1. G
ram
mar
for e
xten
ded
expr
essi
ons
2. S
tere
otyp
es fo
r ext
ende
d da
ta ty
pes
2. C
ompl
ex ti
me
expr
essi
ons
/ MAR
TE T
utor
ial
43
Bas
ic T
extu
al E
xpre
ssio
ns in
VS
L
Valu
e Sp
ec.
Exam
ples
Rea
l Num
ber
1.2E-3
//scientific notation
Dat
eTim
e#12/01/06 12:00:00#
//calendar date time
Col
lect
ion
{1, 2, 88, 5, 2} //sequence, bag, ordered set..
{{1,2,3}, {3,2}} //collection of collections
Tupl
e an
d ch
oice
(value=2.0, unit= ms)
//duration tuple value
periodic(period=2.0, jitte
r=3.3) //arrival pattern
Inte
rval
[1..251[ //upper closed interval between integers
[$A1..$A2] //interval between variables
Varia
ble
decl
arat
ion
& C
all
io$var1 //input/output variable declaration
var1 //variable call expression.
Arit
hmet
icO
pera
tion
Cal
l+(5.0,var1) //”add” operation on Real datatypes
5.0+var1 //infix operator notation
Con
ditio
nal
Expr
essi
on((var1<6.0)?(10^6):1) //if true return 10 exp 6,else 1
Dat
eTim
eR
eal N
umbe
r#12/01/06 12:00:00#
//calendar date time
1.2E-3
//scientific notation
Inte
rval
Tupl
e an
d ch
oice
Col
lect
ion
[1..251[ //upper closed interval between integers
d[$A1..$A2] //interval between variables
(value=2.0, unit= ms)
//duration tuple value
periodic(period=2.0, jitte
r=3.3) //arrival pattern
{1, 2, 88, 5, 2} //sequence, bag, ordered set..
{{1,2,3}, {3,2}} //collection of c
ollections
Varia
ble
decl
arat
ion
& C
all
io$var1 //input/output variable declaration
var1 //variable call expression.
Arit
hmet
icO
pera
tion
Cal
l+(5.0,var1)
//”add” operation on Real datatypes
5.0+var1 //infix operator notation
Con
ditio
nal
Expr
essi
on((var1<6.0)?(10^6):1) //iftrue return 10 e
xp 6,else 1
�E
xten
ded
Prim
itive
Val
ues
�E
xten
ded
Com
posi
te V
alue
s�
Ext
ende
d E
xpre
ssio
ns
/ MAR
TE T
utor
ial
44
VS
L E
xten
ded
Dat
a Ty
pes
VS
L re
uses
UM
L D
ataT
ype
cons
truct
s, b
ut a
dds…
�Bo
unde
dTyp
e�
Inte
rval
Type
�C
olle
ctio
nTyp
e�
Tupl
eTyp
e�
Cho
iceT
ype
leng
th: L
ong
prio
rityR
ange
: Int
eger
Inte
rval
posi
tion:
Inte
gerV
ecto
rsh
ape:
Inte
gerM
atrix
cons
umpt
ion:
Pow
erar
rival
: Arri
valP
atte
rn
MyC
lass
leng
th =
212
333
prio
rityR
ange
= [0
..2]
posi
tion=
{2,3
}sh
ape
= {{
2,3}
,{1,5
}}co
nsum
ptio
n =
(-, e
xp=x
*v1,
uni
t= m
W, s
ourc
e= c
alc)
arriv
al=
perio
dic
(per
iod=
10,
jitte
r= 0
.1)
cl: M
yCla
ss
Spec
ifica
tion
exam
ple…
Dec
lara
tion
exam
ple…
/ MAR
TE T
utor
ial
45
Bou
nded
dura
tion
inte
rval
cons
train
t
Exa
mpl
esof
Tim
e E
xpre
ssio
ns w
ithV
SL
Bou
nded
inst
ant
inte
rval
cons
train
t
Var
iabl
e de
clar
atio
nde
notin
gan
inst
ant
(UM
L Ti
me
Obs
erva
tion)
Dur
atio
nco
nstra
int
Varia
ble
decl
arat
ion
deno
ting
a du
ratio
n(U
ML
Dur
atio
nObs
erva
tion)
/ MAR
TE T
utor
ial
46
Sum
mar
y of
the
“MA
RTE
’sN
FP”
�S
ynth
esis
of b
est m
odel
ing
prac
tices
…�
OC
L: fu
ll co
nstra
int l
angu
age,
but
com
plex
and
not
real
-tim
e or
ient
ed�
SP
T P
rofil
e: b
uilt-
in T
VL
lang
uage
is s
impl
er, b
ut n
ot fl
exib
le�
QoS
&FT
Pro
file:
ann
otat
ion
mec
hani
sm is
flex
ible
, but
com
plex
�N
FP &
VS
L re
use
sele
cted
mod
elin
g fe
atur
es, w
hile
stil
l pro
vidi
ng
sim
plic
ity a
nd fl
exib
ility
�Fo
unda
tions
…�
Reu
se O
CL
cons
truct
s: g
ram
mar
for v
alue
s an
d ex
pres
sion
s�
Gen
eric
dat
a ty
pe s
yste
m: (
base
d on
ISO
’s G
ener
al-P
urpo
se D
atat
ypes
)�
VS
L ex
tend
s U
ML
Sim
ple
Tim
e m
odel
(occ
urre
nce
inde
x, ji
tters
,...)
�Fo
rmal
ly d
efin
ed b
y ab
stra
ct a
nd c
oncr
ete
synt
axes
(gra
mm
ar).
�Fl
exib
ility
: exp
ress
ion
oper
ator
s in
mod
el-s
peci
fic li
brar
ies
/ MAR
TE T
utor
ial
47
Out
lines
of t
he M
AR
TE F
ound
atio
ns
�M
AR
TE fo
unda
tions
�D
efin
e a
set o
f bas
ic c
once
pts
for M
DD
of R
TES
�In
tend
ed to
be
used
as
basi
c la
yer a
t OM
G fo
r fut
ure
RTE
rela
ted
stds
�C
onsi
sts
of fi
ve s
ub-p
rofil
es
/ MAR
TE T
utor
ial
48
�M
AR
TE a
dopt
s m
odel
s of
tim
e th
at re
ly o
n pa
rtial
ord
erin
g of
in
stan
ts�
Thre
e ba
sic
time
mod
els
�C
hron
omet
ric ti
me
mod
el�
Mai
nly
conc
erns
with
tim
e ca
rdin
ality
�E
.g.,
dela
y, d
urat
ion
and
cloc
k tim
e
�Lo
gica
l tim
e m
odel
�M
ainl
y co
ncer
ns e
vent
s or
derin
g.�
E.g
., ev
1 is
bef
ore
ev2
�S
ynch
rono
us ti
me
mod
el�
Spe
cial
izat
ion
of th
e lo
gica
l tim
e m
odel
�In
trodu
ce n
otio
n of
sim
ulta
neity
�E
.g.,
ev1
and
ev2
occu
rs a
t the
sam
e in
stan
t
Sco
pe o
f the
Tim
e P
rofil
e of
MA
RTE
/ MAR
TE T
utor
ial
49
Tim
e P
acka
ges
of M
AR
TE
Def
ine
time
rela
ted
exte
nsio
ns o
f UM
LD
efin
es
time
rela
ted
faci
lites
for u
ser m
odel
s
Def
ines
lit
eral
s us
ed
to
spec
ify th
e w
ay to
inte
rpre
t a
time
expr
essi
on: e
ither
as
a du
ratio
n or
as
an in
stan
t.D
efin
es
liter
als
used
to
sp
ecify
th
e di
scre
te
or
dens
e na
ture
of
a
time
valu
e.
/ MAR
TE T
utor
ial
50
The
Tim
eLib
rary
Mod
el L
ibra
ry<<
mod
elLi
brar
y>>
Tim
eLib
rary
TAI
UT0
UT1
UTC
Loca
lTT TD
BTC
GTC
BS
ider
eal
GP
S
<<en
umer
atio
n>>
Tim
eSta
ndar
dKin
d
curr
entT
ime(
): R
eal
<<cl
ockT
ype>
>{ n
atur
e =
dens
e, u
nitT
ype
= Ti
meU
nitK
ind,
getT
ime
= cu
rren
tTim
e }
Idea
lClo
ck
<<cl
ock>
>{ u
nit =
s }
idea
lClk
:Idea
lClo
ck
«uni
t» s
«uni
t» m
s {b
aseU
nit=
s, c
onvF
acto
r=0.
001}
«uni
t» u
s {b
aseU
nit=
ms,
con
vFac
tor=
0.00
1}«u
nit»
ns
{bas
eUni
t=us
, con
vFac
tor=
0.00
1}«u
nit»
min
{bas
eUni
t=s,
con
vFac
tor=
60}
«uni
t» h
rs {b
aseU
nit=
min
, con
vFac
tor=
60}
«uni
t» d
ys {b
aseU
nit=
hrs,
con
vFac
tor=
24}
«en
umer
atio
n»
Tim
eUni
tKin
d
<<un
it>>
tick
<<en
umer
atio
n>>
Logi
calT
imeU
nitK
ind
valu
e: R
eal
expr
: Clo
cked
Val
ueS
peci
ficat
ion
unit:
TU
Kon
Clo
ck: S
tring
<<tu
pleT
ype>
>Ti
med
Valu
eTyp
e
TUK
<<pr
imiti
ve>>
Clo
cked
Valu
eSpe
cific
atio
n
star
tfin
ish
send
rece
ive
cons
ume
<<en
umer
atio
n>>
Even
tKin
d
Pred
efin
ed
enum
erat
es
rela
ted
to ti
me
E.g
., 1.
5 m
s on
idea
lClk
-Pre
defin
ed c
lock
mod
elin
g tim
e w
hich
is
used
in p
hysi
cal l
aws
(den
se ti
me)
-It
shou
ld b
e im
porte
d in
mod
els
that
re
fer t
o ch
rono
met
ric ti
me.
-Use
d to
type
tim
e-re
late
d pr
oper
ties.
-H
as t
o be
par
amet
eriz
ed a
ccor
ding
to
unit
to u
sed
(TU
K te
mpl
ate
para
met
er).
/ MAR
TE T
utor
ial
51
«C
lock
Type
»�
Use
dto
dec
lare
cloc
kty
pes
of u
ser a
pplic
atio
ns�
Exa
mpl
eof
pro
perti
esde
finin
gth
e cl
ock
type
�na
ture
: Tim
eNat
ureK
ind
[1]
�D
ense
or d
iscr
ete
�is
Logi
cal:
Boo
lean
[1]
�D
efau
lt va
lue
isfa
lse
�re
solA
ttr: P
rope
rty[0
..1]
�R
efer
sa
prop
erty
whi
chsl
ot w
illde
fine
the
cloc
kre
solu
tion
�E
xam
ples
curre
ntTi
me(
): R
eal
«cl
ockT
ype
»{ n
atur
e =
dens
e, u
nitT
ype
= Ti
meU
nitK
ind,
getT
ime
= cu
rrent
Tim
e }
Idea
lClo
ck
natu
re: T
imeN
atur
eKin
d[1
]un
itTyp
e: E
num
erat
ion
[0..1
]is
Logi
cal:
Bool
ean
[1] =
fals
ere
solA
ttr: P
rope
rty [0
..1]
max
ValA
ttr: P
rope
rty [0
..1]
offs
etAt
tr: P
rope
rty [0
..1]
getT
ime:
Ope
ratio
n [0
..1]
setT
ime:
Ope
ratio
n [0
..1]
inde
xToV
alue
: Ope
ratio
n [0
..1]
/ MAR
TE T
utor
ial
52
curr
entT
ime(
): R
eal
«cl
ockT
ype
»{ n
atur
e =
dens
e, u
nitT
ype
= Ti
meU
nitK
ind,
getT
ime
= cu
rren
tTim
e }
Idea
lClo
ck
«C
lock
s»
�C
lock
s ar
e in
stan
ce s
peci
ficat
ion
of C
lock
Type
s�
Clo
cks
prop
ertie
s ar
e:�
stan
dard
: Tim
eSta
ndar
dKin
d[0
..1]
�un
it: U
nit [
0..1
]�
type
: Clo
ckTy
pe[1
]�
Exa
mpl
e
/ MAR
TE T
utor
ial
53
Tim
ed&
Clo
ckC
onst
rain
tsan
d Ti
med
Dom
ain
�«
Tim
edC
onst
rain
t»�
Impo
ses
cons
train
ts o
n ei
ther
inst
ant v
alue
s or
dur
atio
n va
lues
as
soci
ated
with
mod
el e
lem
ents
bou
nd to
clo
cks.
�C
an b
e co
nven
ient
ly e
xpre
ssed
in V
SL
�«
Clo
ckC
onst
rain
t»�
Impo
ses
depe
nden
cy b
etw
een
cloc
ks�
Ref
ers
to a
set
of c
lock
s an
d po
ssib
ly to
oth
er m
odel
ele
men
ts�
Des
crib
ed v
ia a
n op
aque
exp
ress
ion
usin
g a
dedi
cate
d la
ngua
ge�
E.g
. CC
SL
(Clo
ck C
onst
rain
t Spe
cific
atio
n La
ngua
ge) d
efin
ed in
Ann
ex C
�«
Tim
edD
omai
n»
�N
ames
pace
for:
�C
lock
and
Clo
ckTy
pes
user
def
initi
ons
�B
ut a
lso
for C
lock
Con
stra
ints
(see
exam
ple
late
r)�
Mod
el e
lem
ents
of t
he T
imed
Dom
ain
may
refe
r to
Clo
cks
to e
xpre
ss th
at
thei
r beh
avio
r dep
ends
on
time / M
ARTE
Tut
oria
l54
Exa
mpl
eof
Tim
edC
onst
rain
ts
«co
nstra
int»
«tim
edC
onst
rain
t»{ (
t0[i+
1] -
t0[i]
) > (1
00, m
s) }
Tim
ing
cons
train
twith
cond
ition
exp
ress
ion
Dur
atio
n co
nstra
int b
etw
een
two
succ
essi
ve ti
me
inst
ants
/ MAR
TE T
utor
ial
55
Exa
mpl
eof
Tim
edD
omai
nw
itha
Clo
ckC
onst
rain
t
Inst
ants
chr
onog
ram
s of
clo
cks
cc1
and
cc2
curr
entT
ime(
): R
eal
«cl
ockT
ype
»{ n
atur
e =
dens
e, u
nitT
ype
= Ti
meU
nitK
ind,
getT
ime
= cu
rren
tTim
e }
Idea
lClo
ck
/ MAR
TE T
utor
ial
56
Tim
ed In
stan
t and
Dur
atio
n O
bser
vatio
nsR
efer
ence
to
a
time
inst
ant d
urin
g an
exe
cutio
n an
d po
ints
ou
t an
el
emen
t in
th
e m
odel
to o
bser
ve.
Ref
eren
ce
a du
ratio
n du
ring
an
exec
utio
n an
d po
ints
ou
t th
e el
emen
ts
in
the
mod
el to
obs
erve
.
obsK
ind:
Eve
ntK
ind[
0..1
]
«st
ereo
type
»Ti
med
Inst
antO
bser
vatio
n
obsK
ind:
Eve
ntK
ind[
0..2
]
«st
ereo
type
»Ti
med
Dur
atio
nObs
erva
tion
«st
ereo
type
»C
lock
on1
on1
MA
RTE
ob
serv
atio
ns
have
to re
fere
nce
a cl
ock.
MA
RTE
ob
serv
atio
ns
clar
ify w
hich
eve
nts
is t
o ob
serv
e:
/ MAR
TE T
utor
ial
57
Exa
mpl
esof
«Ti
med
Inst
antO
bser
vatio
n»an
d «T
imed
Dur
atio
nObs
erva
tion»
/ MAR
TE T
utor
ial
58
Tim
edE
vent
s an
d P
roce
ssin
gs
Opt
iona
l du
ratio
n va
lue
betw
een
two
succ
essi
ve
occu
rrenc
es
Spe
cifie
s an
ins
tant
in
time
via
an
expr
. an
d m
ay
be
abso
lute
or r
elat
ive.
Bou
nd o
f rep
etiti
ons
/ MAR
TE T
utor
ial
59
Exa
mpl
eof
«Ti
med
Pro
cess
ing»
& «
Tim
edE
vent
»w
ithLo
gica
lTim
e
getC
amP
os( )
: Rea
l
«cl
ockT
ype
»{ n
atur
e =
disc
rete
, isL
ogic
al =
true
, un
itTyp
e =
CA
MLo
gica
lTim
eUni
tKin
d,ge
tTim
e =
getC
amP
os }
CA
MC
lock
«cl
ock
»{ u
nit =
tick
}ca
mC
lk:C
AM
Clo
ck«
unit
» °C
AM
«en
umer
atio
n»
CA
MLo
gica
lTim
eUni
tKin
d
/ MAR
TE T
utor
ial
60
Out
lines
of t
he M
AR
TE F
ound
atio
ns
�M
AR
TE fo
unda
tions
�D
efin
e a
set o
f bas
ic c
once
pts
for M
DD
of R
TES
�In
tend
ed to
be
used
as
basi
c la
yer a
t OM
G fo
r fut
ure
RTE
rela
ted
stds
�C
onsi
sts
of fi
ve s
ub-p
rofil
es
/ MAR
TE T
utor
ial
61
Out
lines
of t
he G
RM
pac
kage
�P
rovi
des
basi
c co
ncep
ts fo
r mod
elin
g a
gene
ral (
high
-leve
l)
plat
form
for p
roce
ssin
g R
TE a
pplic
atio
ns�
Incl
udes
the
feat
ures
for m
odel
ing
proc
essi
ng p
latfo
rms
at d
iffer
ent
leve
l of d
etai
ls.
�Th
e le
vel o
f gra
nula
rity
need
ed d
epen
ds o
n th
e co
ncer
n m
otiv
atin
g th
e de
scrip
tion
of th
e pl
atfo
rm�
E.g
., th
e ty
pe o
f the
pla
tform
, the
type
of t
he a
pplic
atio
n, o
r the
type
of
anal
ysis
to b
e ca
rried
out
on
the
mod
el
�B
uild
in a
bot
tom
-up
proc
ess
to a
bstra
ct fi
ner-
leve
l pla
tform
s�
Pro
cess
ing
plat
form
for d
esig
n co
ncer
n�
See
HR
M a
nd S
RM
�P
roce
ssin
g pl
atfo
rm fo
r ana
lysi
s co
ncer
n�
See
GQ
AM
-rel
ated
ptf
and
furth
er re
finem
ents
for p
erfo
rman
ce a
nd
sche
dula
bilit
yan
alys
is
/ MAR
TE T
utor
ial
62
Ess
ence
of t
he G
RM
Pac
kage
inst
ance
type1..*
0..*
Res
ourc
eIns
tanc
e
Res
ourc
eSer
vice
Exe
cutio
n
0..*1
cont
ext
exeS
ervi
ces
inst
ance
type1..*
0..*
ety
pe*1.
.*
type*
1..*
inst
ance
*0.
.*R
esou
rceI
nsta
nce
Res
ourc
eSer
vice
Exe
cutio
n
*0.
.*1t
cont
ext s
exeS
ervi
ces
ein
stan
ce
*0.
.*
Cla
ssifi
er c
once
rnO
bjec
t con
cern
/ MAR
TE T
utor
ial
63
Out
lines
of t
he M
AR
TE F
ound
atio
ns
�M
AR
TE fo
unda
tions
�D
efin
e a
set o
f bas
ic c
once
pts
for M
DD
of R
TES
�In
tend
ed to
be
used
as
basi
c la
yer a
t OM
G fo
r fut
ure
RTE
rela
ted
stds
�C
onsi
sts
of fi
ve s
ub-p
rofil
es
/ MAR
TE T
utor
ial
64
Allo
catio
n &
Ref
inem
ent
�B
asic
idea
s�
Allo
cate
an
appl
icat
ion
elem
ent t
o an
pro
cess
ing
plat
form
el
emen
t�
Ref
ine
a ge
nera
l ele
men
t int
o on
e or
sev
eral
mor
e sp
ecifi
c el
emen
ts
�In
spire
d by
the
Sys
ML
allo
catio
n�
Can
onl
y al
loca
te a
pplic
atio
n to
exe
cutio
n pl
atfo
rm�
Can
atta
ch N
FP c
onst
rain
ts to
the
allo
catio
n
/ MAR
TE T
utor
ial
65
A tw
o st
ep p
roce
ss fo
r allo
catio
n m
odel
ing
�Id
entif
y po
ssib
le s
ourc
es a
nd ta
rget
s of
allo
catio
ns
�D
efin
e al
loca
tion
rela
tions
hips
and
its
feat
ures
Wha
t can
ser
ve a
s a
targ
et o
f an
allo
catio
n,
the
phys
ical
vie
w:
�a
reso
urce
or a
se
rvic
e.
Wha
t can
be
allo
cate
d, th
e lo
gica
l vi
ew:
�be
havi
or o
r st
ruct
ure
/ MAR
TE T
utor
ial
66
Allo
catio
n ex
ampl
e(1
)
Rea
lTim
eOpe
ratin
gSys
tem
App
licat
ion
myS
peed
Reg
ulat
or :
Spe
edR
egul
ator
Sys
tem
[1]
Spe
edC
ontro
ller
Car
Spe
ed
« sc
hedu
labl
eRes
ourc
e »
OS
_Tas
k«
stor
ageR
esou
rce
»V
irtua
lMem
ory
/ MAR
TE T
utor
ial
67
Allo
catio
n ex
ampl
e(2
)
Rea
lTim
eOpe
ratin
gSys
tem
App
licat
ion
myS
peed
Reg
ulat
or :
Spe
edR
egul
ator
Sys
tem
[1]
« ap
p_al
loca
ted
»S
peed
Con
trolle
r«
app_
allo
cate
d »
Car
Spe
ed
« sc
hedu
labl
eRes
ourc
e,ep
_allo
cate
d »
OS
_Tas
k
« st
orag
eRes
ourc
e,ep
_allo
cate
d »
Virt
ualM
emor
y
/ MAR
TE T
utor
ial
68
Allo
catio
n ex
ampl
e(3
)
Rea
lTim
eOpe
ratin
gSys
tem
App
licat
ion
myS
peed
Reg
ulat
or :
Spe
edR
egul
ator
Sys
tem
[1]
« ap
p_al
loca
ted
»S
peed
Con
trolle
r«
app_
allo
cate
d »
Car
Spe
ed
« sc
hedu
labl
eRes
ourc
e,ep
_allo
cate
d »
OS
_Tas
k
« st
orag
eRes
ourc
e,ep
_allo
cate
d »
Virt
ualM
emor
y
«allo
cate
»{k
ind=
timeS
ched
ulin
g}«a
lloca
te»
{spa
tialD
istri
butio
n}
/ MAR
TE T
utor
ial
69
Allo
catio
n ex
ampl
e(4
)
/ MAR
TE T
utor
ial
70
Out
lines
of t
he M
AR
TE F
ound
atio
ns
�M
AR
TE fo
unda
tions
�D
efin
e a
set o
f bas
ic c
once
pts
for M
DD
of R
TES
�In
tend
ed to
be
used
as
basi
c la
yer a
t OM
G fo
r fut
ure
RTE
rela
ted
stds
�C
onsi
sts
of fi
ve s
ub-p
rofil
es
/ MAR
TE T
utor
ial
71
The
Mar
teG
ener
al C
ompo
nent
Mod
el
�D
efin
ed w
ithin
MA
RTE
to s
uppo
rt C
BS
E a
nd to
pro
vide
a c
omm
on
deno
min
ator
am
ong
vario
us e
xist
ing
com
pone
nt m
odel
s, w
hich
in
prin
cipl
e do
not
targ
et e
xclu
sive
ly th
e R
TE d
omai
n
�In
trodu
ce n
o ne
w c
ompo
nent
-rel
ated
con
cept
s�
Has
to c
ope
with
var
ious
com
pone
nt m
odel
s�
E.g
., U
ML2
, Sys
ML,
Spi
rit, A
AD
L, L
ight
wei
ght-C
CM
, EA
ST-
AD
L2, A
utos
ar, …
�M
ain
feat
ures
�M
ainl
y re
fined
UM
L st
ruct
ured
cla
sses
, on
top
of w
hich
a s
uppo
rtfo
r S
ysM
Lbl
ocks
has
bee
n ad
ded
�B
ut a
lso
com
patib
le w
ith a
sup
port
for L
ight
wei
ght-C
CM
, AA
DL
and
EA
ST-
AD
L2, S
pirit
and
Aut
osar
�S
hortc
uts
for U
ML2
mod
elin
g of
com
pone
nts/
com
posi
tes
diag
ram
s
/ MAR
TE T
utor
ial
72
Exa
mpl
e of
Nor
mal
UM
L2 S
truct
ured
Cla
ss
Nor
mal
UM
L2 p
ort
type
dby
a c
lass
rea
lizin
gan
d us
ing
inte
rface
s:
the
port
rppr
ovid
esP
Reg
Inte
rface
and
requ
ired
RR
egul
ator
Inte
rface
UM
L2 s
truct
ured
cla
ssUM
L2 p
ort
/ MAR
TE T
utor
ial
73
The
MA
RTE
GC
M S
ub-p
rofil
e
: con
cept
s fo
r dat
a-ba
sed
flow
com
mun
icat
ion
: con
cept
s fo
r mes
sage
-bas
edflo
w c
omm
unic
atio
n
/ MAR
TE T
utor
ial
74
Exa
mpl
esof
Flo
wP
ortu
sage
�A
tom
icflo
w p
ort e
xam
ple
�N
on-a
tom
icflo
w p
ort e
xam
ple
/ MAR
TE T
utor
ial
75
Exa
mpl
ew
ith«
Mes
sage
Por
t»
/ MAR
TE T
utor
ial
76
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
77
RTE
Mod
el o
f Com
puta
tion
& C
omm
unic
atio
n
�H
igh-
leve
l mod
elin
g co
ncep
ts w
here
RT/
E c
once
rns
are
embe
dded
insi
de m
odel
ing
artif
acts
(E.g
., U
ML
activ
e/pa
ssiv
e ob
ject
s)�
Impl
icit
sem
antic
s�
Two
fam
ilies
of c
once
rns
�Q
uant
itativ
e as
pect
s�
E.g
. con
curr
ency
and
beh
avio
r�
Qua
litat
ive
aspe
cts
as re
al-ti
me
feat
ure
�E
.g. d
eadl
ine
or p
erio
d
�P
acka
ge d
epen
denc
ies
/ MAR
TE T
utor
ial
78
Qua
litat
ive
RT
feat
ures
Mod
elin
g
�«
RtU
nit»
�G
ener
aliz
atio
n of
UM
L2 A
ctiv
e O
bjec
ts�
Aut
onom
ous
proc
essi
ng re
sour
ce, a
ble
to
hand
le d
iffer
ent m
essa
ges
at th
e sa
me
time
�O
wns
at l
ast o
ne s
ched
ulab
le re
sour
ce�
Man
aged
eith
er s
tatic
ally
(via
a S
R p
ool)
or d
ynam
ical
ly�
May
hav
e op
erat
iona
l mod
e de
scrip
tion
�M
ay b
e m
ain
obje
cts
�R
efer
to th
e m
ain
oper
atio
n
�«
PpU
nit»
�G
ener
aliz
atio
n of
UM
L2 P
assi
ve O
bjec
ts�
Con
curr
ency
pol
icy
spec
ified
eith
er lo
cally
or g
loba
lly�
Pro
cess
ing
is e
ither
imm
edia
teR
emot
eor
def
erre
d
/ MAR
TE T
utor
ial
79
Qua
ntita
tive
RT
feat
ures
mod
elin
g �
«R
ealT
imeF
eatu
re»
�«
Rte
Con
nect
or»
/ MAR
TE T
utor
ial
80
The
Arri
valP
atte
rn d
ata
type
�D
efin
edin
the
MA
RTE
_Lib
rary
/ MAR
TE T
utor
ial
81
Dyn
amic
s as
pect
s of
«R
tUni
t»an
d «
PpU
nit»
�S
ervi
ces
spec
ifica
tion
�C
lass
ifier
beh
avio
r spe
cific
atio
n
isA
tom
ic: B
oole
an [1
] = fa
lse
conc
Pol
icy:
Con
curr
ency
Kin
dex
eKin
d: E
xecu
tionK
ind
sync
hKin
d: S
ynch
roni
zatio
nKin
d
RtS
ervi
ce
defe
rred
rem
oteI
mm
edia
telo
calIm
med
iate
«en
umer
atio
n»
Exec
utio
nKin
d
read
erw
riter
para
llel
«en
umer
atio
n»
Con
curr
ency
Kin
dsy
nchr
onou
sas
ynch
rono
usde
laye
dSyn
chro
nous
rend
ezV
ous
othe
r«en
umer
atio
n»
Sync
hron
isat
ionK
ind
«m
etac
lass
»U
ML2
::Beh
avio
ralF
eatu
re
/ MAR
TE T
utor
ial
82
Usa
ge e
xam
ples
of t
he R
TEM
oCC
exte
nsio
ns (1
)
Cru
iseC
ontr
olSy
stem
getS
peed
(): S
peed
«pp
Uni
t»{c
oncP
olic
y=gu
arde
d}Sp
eedo
met
er
«rtS
ervi
ce»
{exe
Kin
d=de
ferre
d} s
tart(
)«r
tSer
vice
» {e
xeK
ind=
defe
rred}
sto
p()
tgS
peed
: Spe
ed«rt
Uni
t»C
ruis
eCon
trol
er 1sp
m
«da
taTy
pe»
Spee
d
star
tDet
ectio
n()
stop
Det
ectio
n()
«rt
Uni
t»O
bsta
cleD
etec
tor
1sp
m
isD
ynam
ic =
fals
eis
Mai
n =
fals
epo
olSi
ze =
10
pool
Pol
icy
= cr
eate
isM
ain
= tru
em
ain
= st
art
/ MAR
TE T
utor
ial
83
Usa
ge e
xam
ples
of t
he R
TEM
oCC
exte
nsio
ns (2
)
sd C
ruis
eCon
trol
Star
t
:Cru
iseC
ontro
l:S
peed
omet
er
star
t()
@t0
star
tAcq
uisi
tion(
)
getS
peed
()
Spee
doc
cKin
d =
perio
dic
(per
iod=
(10,
ms)
, jitt
er=(
2, u
s))
valu
e =
(tRef
=t0,
relD
l=(1
0, m
s), m
iss=
(1, %
, max
))
occK
ind
= pe
riodi
c (p
erio
d=(1
0, m
s), j
itter
=(2,
us)
)va
lue
= (tR
ef=t
0, re
lDl=
(10,
ms)
, mis
s=(1
, %, m
ax))
occK
ind
= ap
erio
dic
()va
lue
= (tR
ef=t
0, re
lDl=
(10,
ms)
, mis
s=(1
, %, m
ax))
/ MAR
TE T
utor
ial
84
Usa
ge e
xam
ples
of t
he R
TEM
oCC
exte
nsio
ns (3
)co
mpu
teTr
ajec
tory
getL
ocat
ion
«rtA
ctio
n, rt
f»ge
tFlig
htP
lan
«rtA
ctio
n, rt
f»pe
rform
Com
puta
tion
@t0
«rtA
ctio
n, rt
f»ge
nera
teC
omm
and
prio
rity=
1oc
cKin
d =
perio
dic
(per
iod=
(10,
ms)
, jitt
er=(
2,us
))re
lDl=
(1,m
s)tR
ef=t
0m
iss=
(1, %
, max
)sy
ncK
ind=
sync
hron
ous
prio
rity=
1oc
cKin
d =
perio
dic
(per
iod=
(10,
ms)
, jitt
er=(
2,us
))re
lDl=
(3,m
s)tR
ef=t
0m
iss=
(1, %
, max
)sy
ncK
ind=
dela
yedS
ynch
rono
us
prio
rity=
1oc
cKin
d =
perio
dic
(per
iod=
(10,
ms)
, jitt
er=(
2,us
))re
lDl=
(4,m
s)tR
ef=t
0m
iss=
(1, %
, max
)sy
ncK
ind=
sync
hron
ous
/ MAR
TE T
utor
ial
85
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
86
Det
aille
dR
esou
rce
Mod
elin
g w
ithin
MA
RTE
/ MAR
TE T
utor
ial
87
Wha
t is
the
Sof
twar
e R
esou
rce
Mod
elin
g P
rofil
e (S
RM
) ?
�A
UM
L pr
ofile
for m
odel
ing
AP
Is o
f RT/
E s
wex
ecut
ion
supp
orts
�R
eal T
ime
Ope
ratin
g S
yste
ms
(RTO
S)
�D
edic
ated
Lan
guag
e Li
brar
ies
(e.g
. AD
A)
�B
UT
it is
NO
T a
new
AP
I sta
ndar
d de
dica
ted
to th
e R
T/E
dom
ain!
�SR
M =
a un
ified
mea
n to
des
crib
e su
ch e
xist
ing
or p
ropr
ieta
ry A
PIs
�In
whi
ch s
teps
sha
ll I u
se S
RM
?
/ MAR
TE T
utor
ial
88
Item
s sh
own
in n
ext s
lides
�S
RM
ove
rvie
w
�D
etai
ls o
f SR
M th
roug
h th
e O
SE
K/V
DX
cas
e st
udy
�In
tere
sts
to u
se S
RM
thro
ugh
exam
ples
�M
odel
of m
ultit
ask
desi
gn�
E.g
., a
robo
tic c
ase
stud
y�
Gen
erat
ion
of O
S c
onfig
urat
ion
file
�E
.g.,
OS
EK
OIL
con
figur
atio
n fil
e ge
nera
tion
�P
ortin
g ex
istin
g R
TE a
pplic
atio
n m
odel
s�
E.g
., fro
m O
SE
K to
AR
INC
/ MAR
TE T
utor
ial
89
Mai
n ex
pect
ed u
se c
ases
of S
RM
Softw
are
Des
igne
r
Des
crib
e ex
ecut
ion
supp
ort A
PI
«in
clud
e»
Cod
e ge
nera
tion
Exec
utio
n Pl
atfo
rm P
rovi
der
M
etho
dolo
gy
Pro
vide
r
Mod
el
Tran
sfor
mat
ion
«ex
tend
»
Use
A
PI m
odel
«
exte
nd»
/ MAR
TE T
utor
ial
90
Why
sha
ll I u
se S
RM
for m
odel
ing
RTO
S A
PIs
?
�R
TOS
AP
I mod
elin
g w
ith U
ML
is a
lread
y po
ssib
le�
But
, gen
eric
s U
ML
is la
ckin
g R
TE n
ativ
e ar
tifac
ts!
�N
o m
odel
ing
artif
acts
to d
escr
ibe
spec
ific
conc
epts
�E
.g. t
asks
, sem
apho
res
and
mai
lbox
es
�C
onse
quen
tly, m
odel
s re
lies
only
on
nam
ing
conv
entio
ns�
Not
pos
sibl
e to
def
ine
gene
ric to
ols
usin
g th
ese
mod
els
�E
.g. c
ode
gene
rato
r or m
odel
tran
sfor
mat
ions
for a
naly
sis.
�H
ence
, SR
M p
rofil
e al
low
s:�
To m
odel
pre
cise
mul
titas
king
des
igns
�To
be
able
to d
escr
ibe
gene
ric g
ener
ativ
e to
ols
�To
des
crib
e S
W e
xem
odel
sin
an
unifi
ed a
nd s
tand
ard
way
�S
RM
pro
file
is a
sub
-pro
file
of th
e M
AR
TE s
tand
ard
/ MAR
TE T
utor
ial
91
SRM
SW_C
oncu
rren
cy
GR
M
«im
port
»
SW_B
roke
ring
SW_I
nter
actio
n
SW_R
esou
rceC
ore
«im
port
»«
impo
rt»
«im
port
»
Wha
t is
supp
orte
d by
the
SR
M p
rofil
e ?
Inte
ract
ion
s be
twee
n c
oncu
rren
t co
nte
xts:
•Co
mm
unic
atio
n�
Shar
ed d
ata
�M
essa
ge (
~M
essa
ge q
ueue
)•
Sync
hron
izat
ion
�M
utua
l Exc
lusi
on (
~Se
map
hore
)�
Not
ifica
tion
Res
ourc
e (~
Even
t m
echa
nism
)
Con
curr
ent
exec
uti
on c
onte
xts:
•Sc
hedu
labl
e Res
ourc
e (~
Task
)•
Mem
ory
Part
ition
(~
Proc
ess)
•In
terr
upt
Res
ourc
e•
Alar
m
Har
dwar
e an
d so
ftw
are
reso
urc
es b
roke
rin
g:•
Driv
ers
•M
emor
y m
anag
emen
t
/ MAR
TE T
utor
ial
92
Sna
psho
t of t
he U
ML
exte
nsio
ns p
rovi
ded
by S
RM
SRM
::SW
_B
roke
rin
gSR
M::
SW_
Inte
ract
ion
«M
essa
geC
omR
esou
rce
»«
Not
ifica
tionR
esou
rce
»
«S
hare
dDat
aRes
ourc
e»
«S
wM
utua
lExc
lusi
onR
esou
rce
»
SRM
::SW
_C
oncu
rren
cy
/ MAR
TE T
utor
ial
93
The
OS
EK
/VD
X c
ase
stud
y
�O
SE
K/V
DX
sta
ndar
d (h
ttp://
ww
w.o
sek-
vdx.
org)
�A
utom
otiv
e in
dust
ry a
sta
ndar
d fo
r an
open
-end
ed a
rchi
tect
ure
for d
istri
bute
d co
ntro
l uni
ts in
veh
icle
s
�O
SE
K/V
DX
arc
hite
ctur
e co
nsis
ts o
f thr
ee la
yers
:�
OS
EK
-CO
M la
yer:
Com
mun
icat
ion
�D
ata
exch
ange
sup
port
with
in a
nd b
etw
een
elec
troni
cs c
ontro
l uni
ts
(EC
Us)
�O
SE
K-N
M la
yer :
Net
wor
k M
anag
emen
t�
Con
figur
atio
n de
term
inat
ion
and
mon
itorin
g�
OS
EK
-OS
laye
r: O
pera
ting
Sys
tem
�A
PI s
peci
ficat
ion
of R
TOS
for a
utom
otiv
e E
CU
/ MAR
TE T
utor
ial
94
Ove
rvie
w o
f the
OS
EK
/VD
X-O
S la
yer
�M
ain
char
acte
risic
s�
A s
ingl
e pr
oces
sor o
pera
ting
syst
em�
A s
tatic
RTO
S w
here
all
kern
el o
bjec
ts a
re c
reat
ed a
t com
pile
tim
e
�M
ain
artif
acts
�S
uppo
rt fo
r con
curre
nt c
ompu
ting
�Ta
sk�
A ta
sk p
rovi
des
the
fram
ewor
k fo
r the
exe
cutio
n of
func
tions
�In
terru
pt�
Mec
hani
sm fo
r pro
cess
ing
asyn
chro
nous
eve
nts
�A
larm
& C
ount
er�
Mec
hani
sms
for p
roce
ssin
g re
curri
ng e
vent
s�
Sup
port
for s
ynch
roni
zatio
ns o
f con
curre
nt c
ompu
ting
�E
vent
�M
echa
nism
for c
oncu
rrent
pro
cess
ing
sync
hron
izat
ion
�R
esou
rce
�M
echa
nism
for m
utua
l con
curre
nt a
cces
s ex
clus
ion
/ MAR
TE T
utor
ial
95
Focu
s on
the
OS
EK
/VD
X T
ask
defin
ition
�S
eman
tic�
An
OS
EK
-VD
X ta
sk p
rovi
des
the
fram
ewor
k fo
r com
putin
g ap
plic
atio
n fu
nctio
ns. A
sch
edul
er w
ill o
rgan
ize
the
sequ
ence
of t
ask
exec
utio
ns.
�E
xam
ple
of p
rope
rties
�Pr
iorit
y: U
INT3
2�
Prio
rity
exec
utio
n of
the
task
�St
ackS
ize:
UIN
T32
�S
tack
siz
e as
soci
ated
to th
e ex
ecut
ion
of th
e ta
sk
�E
xam
ple
of p
rovi
ded
serv
ices
�A
ctiv
ateT
ask
(Tas
kID
: Tas
kTyp
e)�
Sw
itch
the
task
, ide
ntifi
ed b
y th
e Ta
skID
para
met
er, f
rom
sus
pend
ed to
read
y st
ate
�C
hain
Task
(Tas
kID
: Tas
kTyp
e)�
Term
inat
e of
the
callin
g ta
sk a
nd a
ctiv
ate
the
task
iden
tifie
d by
the
Task
IDpa
ram
eter
/ MAR
TE T
utor
ial
96
SRM
SW_C
oncu
rren
cy
GR
M
«im
port
»
SW_B
roke
ring
SW_I
nter
actio
n
SW_R
esou
rceC
ore
«im
port
»«
impo
rt»
«im
port
»
Whi
ch S
RM
con
cept
s fo
r OS
EK
Tas
k?
Con
curr
ent
exec
uti
on c
onte
xts:
•Sc
hedu
labl
e Res
ourc
e (~
Task
)•
Mem
ory
Part
ition
(~
Proc
ess)
•In
terr
upt
Res
ourc
e•
Alar
m
/ MAR
TE T
utor
ial
97
Det
ails
of «
Sw
Sch
edul
able
Res
ourc
e»�
Sem
antic
(from
MA
RTE
::SR
M::C
oncu
rrenc
ypa
ckag
e)�
Res
ourc
e w
hich
exe
cute
s, p
erio
dica
lly o
r not
, con
curre
ntly
to o
ther
con
curre
nt re
sour
ces
==>
SRM
art
ifact
s fo
r mod
elin
g O
SEK
-VD
X Ta
sk!
�M
ain
feat
ures
�O
wns
an
entry
poi
nt re
fere
ncin
g th
e ap
plic
atio
n co
de to
exe
cute
�M
ay b
e re
stric
ted
to e
xecu
te in
a g
iven
add
ress
spa
ce (i
.e. a
mem
ory
parti
tion)
�O
wns
pro
perti
es�
E.g
., P
riorit
y, D
eadl
ine,
Per
iod
and
Sta
ckS
ize
�P
rovi
des
serv
ices
�E
.g.,
activ
ate,
resu
me
and
susp
end
�E
xtra
ct fr
om th
e S
RM
::Sw
Con
curr
ency
met
a m
odel
/ MAR
TE T
utor
ial
98
Mod
el o
f an
OS
EK
Tas
kw
ith«S
wS
ched
ulab
leR
esou
rce»
1.D
efin
e a
UM
L m
odel
for O
SE
K_V
DX
::Tas
ka.
Add
mod
el li
brar
y ap
plyi
ng th
e S
RM
pro
file
b.A
dd a
cla
ss a
nd d
efin
es it
s fe
atur
es (p
rope
rties
and
ope
ratio
ns)
2.A
pply
ing
the
«Sw
Sch
edul
able
Res
ourc
e»st
ereo
type
3.Fu
llfill
the
tagg
ed v
alue
s of
the
appl
ied
ster
eoty
pe
(Ste
p 1)
(Ste
p 2)
(Ste
p 3)
Mod
els
have
bee
n re
aliz
ed w
ith th
e P
apyr
us
Ecl
ipse
-bas
ed o
pen-
sour
ce to
ol fo
r UM
L2:
http
://w
ww
.pap
yrus
uml.o
rg
/ MAR
TE T
utor
ial
99
SR
M m
odel
ling
faci
litie
s�
How
to m
odel
mul
tiple
s ca
ndid
ates
for t
he s
ame
sem
antic
?�
Ans
wer
: A
ll st
ereo
type
tags
hav
e m
ultip
le m
ultip
liciti
es. T
hus,
it is
pos
sibl
e to
re
fere
nce
mul
tiple
can
dida
tes
for t
he s
ame
tag.
�E
xam
ples
�B
oth
nam
eat
tribu
tes
and
task
Idpa
ram
eter
are
task
iden
tifie
r
�B
oth
activ
ateT
ask
and
chai
nTas
kop
erat
ions
are
task
act
ivat
ing
serv
ices
+ ac
tivat
eTas
k(ta
skID
task
Type
)+c
hain
Task
()
« sw
Sch
edul
able
Res
ourc
e»
Task
«sw
Sch
edul
able
Res
ourc
e»
activ
ateS
ervi
ce =
act
ivat
eTas
k, c
hain
Task
/ MAR
TE T
utor
ial
100
SR
M m
odel
ing
faci
litie
s (s
eq.)
�H
ow to
mod
el a
feat
ure
whi
ch h
ave
mul
tiple
sem
antic
?�
Ans
wer
: Fe
atur
e ca
n be
refe
renc
ed b
y se
vera
l diff
eren
t tag
s�
Exa
mpl
e�
The
chai
nTas
kse
rvic
e is
bot
h a
term
inat
e se
rvic
e an
d an
act
ivat
e se
rvic
e
�Is
it p
ossi
ble
to re
fere
nce
a fe
atur
e ev
en if
the
feat
ure
owne
r is
not t
he s
tere
otyp
ed
elem
ent ?
�A
nsw
er :
Yes
, the
re is
no
cons
train
ts o
n th
e fe
atur
e ow
ner
�S
RM
allo
ws
mul
tiple
usa
ges
�U
ser c
an u
se c
onst
rain
ts, s
uch
as O
CL
rule
s, to
lim
it th
ose
poss
ibilit
ies
+ ac
tivat
eTas
k(ta
skID
task
Type
)+
chai
nTas
k()
« sw
Sch
edul
able
Res
ourc
e»
Task
«sw
Sch
edul
able
Res
ourc
e»
activ
ateS
ervi
ce =
activ
ateT
ask,
cha
inTa
skte
rmin
ateS
ervi
ce =
chai
nTas
k
+act
ivat
eTas
k()
«in
terfa
ce»
Task
Ser
vice
«S
wS
ched
ulab
leR
esou
rce
» p
riorit
yEle
men
ts =
prio
rity
activ
ateS
ervi
ce =
activ
ateT
ask(
)
-prio
rity
: Int
eger
«sw
Sch
edul
able
Res
ourc
e»
Task
/ MAR
TE T
utor
ial
101
Focu
s on
the
OS
EK
/VD
X E
vent
def
initi
on�
Sem
antic
:�
The
even
t mec
hani
sm is
a m
eans
of s
ynch
roni
satio
nth
at in
itiat
es s
tate
tra
nsiti
ons
of ta
sks
to a
nd fr
om th
e w
aitin
gst
ate.
�E
xam
ple
of o
wne
d pr
oper
ties
�M
ask
: Eve
ntM
askT
ype
�D
efin
e th
e m
ask
asso
ciat
ed w
ith th
e ev
ent
�E
xam
ples
of p
rovi
ded
serv
ices
�Se
tEve
nt(T
askI
D: T
askT
ype,
Mas
k: E
vent
Mas
kTyp
e)�
The
even
ts o
f the
task
refe
renc
ed b
y th
e Ta
skID
para
met
er a
re s
et a
ccor
ding
to th
e ev
ent m
ask
spec
ified
by
the
Mas
kpa
ram
eter
.�
Cal
ling
the
serv
ice
SetE
vent
caus
es th
e ta
sk id
entif
ied
by th
e Ta
skID
para
met
er to
be
tran
sfer
red
to th
e re
ady
stat
e, if
it w
as w
aitin
g fo
r at l
east
one
of t
he e
vent
s sp
ecifi
ed in
the
Mas
kpa
ram
eter
.�
Wai
tEve
nt(M
ask:
Eve
ntM
askT
ype)
�Th
e st
ate
of th
e ca
lling
task
is s
et to
wai
ting,
unl
ess
at le
ast o
ne o
f the
eve
nts
spec
ified
in th
e M
ask
para
met
er h
as a
lread
y be
en s
et.
/ MAR
TE T
utor
ial
102
SRM
SW_C
oncu
rren
cy
GR
M
«im
port
»
SW_B
roke
ring
SW_I
nter
actio
n
SW_R
esou
rceC
ore
«im
port
»«
impo
rt»
«im
port
»
Whi
ch S
RM
con
cept
s fo
r OS
EK
Eve
nt?
Inte
ract
ion
s be
twee
n c
oncu
rren
t co
nte
xts:
•Co
mm
unic
atio
n�
Shar
ed d
ata
�M
essa
ge (
~M
essa
ge q
ueue
)•
Sync
hron
izat
ion
�M
utua
l Exc
lusi
on (
~Se
map
hore
)�
Not
ifica
tion
Res
ourc
e (E
vent
mec
hani
sm)
/ MAR
TE T
utor
ial
103
Det
ails
of «
Not
ifica
tionR
esou
rce»
�S
eman
tic�
Not
ifica
tionR
esou
rce
supp
orts
con
trol f
low
by
notif
ying
the
occu
rren
ces
of
cond
ition
s to
aw
aitin
g co
ncur
rent
reso
urce
s==
> S
RM
arti
fact
s fo
r mod
elin
g O
SE
K-V
DX
Eve
nt!
�M
ain
feat
ures
�E
xam
ples
of o
wne
d at
tribu
te�
mas
kEle
men
tsan
d m
echa
nism
�E
xam
ples
of p
rovi
ded
serv
ice
�flu
shS
ervi
ces,
sig
nalS
ervi
ces,
wai
tSer
vice
san
d cl
earS
ervi
ces
�E
xtra
ct fr
om th
e S
RM
::Sw
Inte
ract
ion
met
a m
odel
/ MAR
TE T
utor
ial
104
OS
EK
/VD
X E
vent
as
a N
otifi
catio
nRes
ourc
e
(i) S
tere
otyp
eic
on
(ii) S
tere
otyp
esh
ape
/ MAR
TE T
utor
ial
105
In w
hich
typi
cal c
ases
sha
ll I u
se S
RM
?
Softw
are
Des
igne
r
Des
crib
e ex
ecut
ion
supp
ort A
PI
«in
clud
e»
Cod
e ge
nera
tion
Exec
utio
n Pl
atfo
rm P
rovi
der
M
etho
dolo
gy
Pro
vide
r
Mod
el
Tran
sfor
mat
ion
«ex
tend
»
Use
A
PI m
odel
«
exte
nd»
/ MAR
TE T
utor
ial
106
Use
exa
mpl
es o
f one
RTO
S m
odel
ed w
ith S
RM
�E
xam
ple
1: M
odel
-bas
ed d
esig
n of
mul
titas
k ap
plic
atio
ns�
Illus
trate
d on
a ro
bot c
ontro
ller a
pplic
atio
n
�E
xam
ple
2: O
S c
onfig
urat
ion
file
gene
ratio
n�
Gen
erat
ion
of th
e O
SE
K O
IL c
onfig
urat
ion
files
�E
xam
ple
3: A
ssis
tanc
e to
por
t app
licat
ions
�Fr
om O
SE
K to
AR
INC
mul
titas
k de
sign
/ MAR
TE T
utor
ial
107
Cas
e st
udy:
A s
impl
e ro
bot c
ontro
ller s
oftw
are
�G
oal
�A
mot
ion
cont
rolle
r sys
tem
for a
n ex
plor
atio
n au
tono
mou
s m
obile
robo
t.�
Rob
ot fe
atur
es�
Pio
neer
Rob
ot (P
3AT)
�Fo
ur d
rivin
g w
heel
s�
A c
amer
a�
Eig
ht s
onar
sen
sors
, etc
.
�D
esig
n fe
atur
es o
f the
robo
t con
trolle
r�
OS
EK
/VD
X e
xecu
tion
supp
ort
�S
imul
atio
n on
Tra
mpo
line
(http
://tra
mpo
line.
rts-s
oftw
are.
org/
)�
Two
perio
dic
task
s�
Dat
a ac
quis
ition
task
�G
et p
ositi
on d
ata
from
son
ar s
enso
rs
ever
y 1
ms
�tra
ject
ory
com
putin
g ta
sk�
Set
new
spe
ed e
very
4 m
s
Rob
ot S
imul
ator
http
://pl
ayer
stag
e.so
urce
forg
e.ne
t/gaz
ebo/
gaze
bo.h
tml
/ MAR
TE T
utor
ial
108
Pur
pose
and
cont
exto
f the
exa
mpl
e1
�P
rovi
de a
mul
titas
k de
sign
of t
he ro
bot c
ontro
ller
�Ta
rget
of t
he d
esig
n is
an
OS
EK
/VD
X-b
ased
pla
tform
�D
esig
n pr
oces
s�
A p
latfo
rm p
rovi
der s
uppl
ies
the
OS
EK
/VD
X m
odel
libr
ary
�M
odel
libr
ary
is d
escr
ibed
with
the
SR
M P
rofil
e (a
s pr
evio
usly
sho
wn)
�A
use
r des
igns
a m
ultit
ask
mod
el o
f the
app
licat
ion
�S
tep
1: D
escr
ibe
the
appl
icat
ion
mod
el (a
lso
calle
d fu
nctio
nal m
odel
)
�S
tep
2: P
ropo
se a
mul
titas
k de
sign
usi
ng th
e O
SE
K m
odel
libr
ary
artif
act
/ MAR
TE T
utor
ial
109
App
licat
ion
desi
gn�
App
licat
ion
mod
el a
t the
func
tiona
l lev
el�
One
robo
t con
trolle
r ent
ity
�A
ims
at c
ontro
lling
the
robo
t mot
ions
�M
ain
func
tions
�A
cqui
re th
e so
nar d
ata
�C
ompu
te th
e ne
w s
peed
of e
ach
4 m
otio
ns a
nd s
end
new
ord
ers
�A
robo
t driv
er e
ntity
�A
ims
at in
terfa
cing
robo
t sen
sors
and
act
uato
rs w
ith th
e co
ntro
lapp
licat
ion
Acq
uire
son
ar d
ata
from
sen
sors
Com
pute
the
4m
otio
nsp
eed
valu
es
Term
inat
e a
mis
sion
Driv
er to
inte
rface
se
nsor
s an
d ac
tuat
ors
/ MAR
TE T
utor
ial
110
Prin
cipl
es o
f the
app
lied
mul
titas
k de
sign
�Tw
o pe
riodi
c ta
sks
�Fo
r dat
a ac
quis
ition
�G
et p
ositi
on d
ata
from
son
ar s
enso
rs�
Ent
ry p
oint
�O
pera
tion
Mot
ionC
ontro
ller::
acqu
ire()
�Pe
riodi
c�
Per
iod
= 1
ms
�Fo
r tra
ject
ory
cont
rol
�C
ompu
te a
nd a
ssig
n ne
w s
peed
ord
er�
Ent
ry p
oint
�O
pera
tion
Mot
ionC
ontro
ller::
traje
ctor
yCon
trol()
�Pe
riodi
c�
Per
iod
= 4
ms
/ MAR
TE T
utor
ial
111
Per
iodi
cta
skin
OS
EK
/VD
X�
A d
esig
n pa
ttern
for i
mpl
emen
ting
perio
dic
task
on
OS
EK
/VD
X-
base
d pl
atfo
rms
�O
ne O
SE
K/V
DX
Cou
nter
�C
ount
er p
erio
d =
perio
d of
the
requ
ired
perio
dic
task
�O
ne O
SE
K/V
DX
Tas
k�
Ent
ry p
oint
: pe
riodi
c ta
sk E
ntry
Poi
nt�
One
OS
EK
/VD
X A
larm
�A
utoS
tart
: Trig
gere
d by
the
coun
ter
�A
ctio
n : A
ctiv
ate
the
task
SR
M P
rofil
e is
use
dto
des
crib
e th
e pa
ttern
/ MAR
TE T
utor
ial
112
Bas
ic R
obot
Con
trolle
r tas
km
odel
s
Per
iod
of th
e pe
riodi
c ta
sk a
cqui
sitio
n : 1
ms
SR
M s
tere
otyp
e to
bin
d ap
plic
atio
n an
d pl
atfo
rm
/ MAR
TE T
utor
ial
113
Exa
mpl
e2:
OS
EK
Con
figur
atio
n Fi
le g
ener
atio
n�
Pur
pose
�G
ener
atio
n of
the
OS
EK
OIL
con
figur
atio
n fil
es fr
om th
e m
ulti-
task
des
ign
of th
e ro
bot
cont
rolle
r�
OIL
: OS
EK
Impl
emen
tatio
n La
ngua
ge�
http
:://o
sek-
vdx.
org
�Th
e go
al o
f OIL
is to
pro
vide
a m
echa
nism
to
con
figur
e an
OS
EK
app
licat
ion
for a
pa
rticu
lar C
PU
�
Prin
cipl
e�
For e
ach
CP
U, t
here
mus
t be
an O
IL
desc
riptio
n�
All
OS
EK
sys
tem
obj
ects
are
des
crib
ed
usin
g O
IL o
bjec
ts�
OIL
des
crip
tions
may
be
:�
hand
-writ
ten
�or
gen
erat
ed b
y a
syst
em
conf
igur
atio
n to
ol
OIL
_VER
SIO
N=
"2.5
" : "R
obot
Con
trolle
r" ;
IMPL
EMEN
TATI
ON
OSE
K {
}; CPU
cpu
{A
PPM
OD
E st
d{
};
CO
UN
TER
cou
nter
{M
AXA
LLO
WED
VALU
E =
255
;TI
CK
SPER
BA
SE =
1 ;
MIN
CYC
LE=
1 ;
}; ALA
RM
alar
mAc
qu{
CO
UN
TER
= c
ount
er;
AC
TIO
N=
AC
TIVA
TETA
SK {
TASK
= a
cqui
sitio
n ;
} ; AU
TOST
AR
T =
TR
UE
{AL
ARM
TIM
E =
1 ;
CY
CLE
TIM
E =
1 ;
APP
MO
DE
= s
td;
} ;}; TA
SKac
quis
ition
{PR
IOR
ITY
= 2
;SC
HED
ULE
= F
ULL
;A
CTI
VATI
ON
= 1
0 ;
AU
TOST
AR
T =
FAL
SE ;
STA
CK
SIZE
= 3
2768
;};
…
/ MAR
TE T
utor
ial
114
Prin
cipl
eto
go
from
a U
ML
mod
el to
an
OIL
file
E.g
. AC
CE
LEO
ww
w.a
ccel
eo.o
rg
OIL
File
Gen
erat
ion
OS
EK
Com
pile
r
Exe
cuta
ble
file
Pla
tform
S
peci
ficm
odel
us
ing
SR
M
Tem
plat
e de
scrip
tion
base
don
SR
M p
rofil
e
OIL
des
crip
tion
file
Use
r sou
rce
code
/ MAR
TE T
utor
ial
115
Exa
mpl
e of
an
OIL
gen
erat
ion
tem
plat
e
Task
acqu
isiti
on{
} Task
traje
ctor
yCon
trolle
r{}
«Sw
Sche
dula
bleR
esou
rce
»B
asic
Task
acqu
isiti
on :
Bas
icTa
sk
traje
ctor
yCon
trolle
r : B
asic
Task
«in
stan
ceO
f»
«in
stan
ceO
f»
For e
ach
UM
L In
stan
ceS
peci
ficat
ion
{if
itscl
assi
fier h
as th
e S
RM
Sw
Sch
edul
able
Res
ourc
est
ereo
type
then
{ge
nera
teTe
xt{
Task
<Ins
tanc
e N
ame>
{}}e
ndG
ener
ate
}end
if}
AC
CE
LEO
/ MAR
TE T
utor
ial
116
Gen
erat
ion
of th
e O
IL fi
le in
the
Pap
yrus
UM
L To
ol
/ MAR
TE T
utor
ial
117
Exa
mpl
e 3:
Ass
ist u
ser t
o po
rt m
ultit
ask
desi
gns
�P
urpo
se�
Ass
ist u
ser t
o po
rt th
e m
ultit
ask
desi
gn to
an
AR
INC
-653
RTO
S�
AR
INC
653
sta
ndar
d pr
ovid
es a
vion
ics
appl
icat
ion
softw
are
with
the
set o
f ba
sic
serv
ices
to a
cces
s th
e op
erat
ing
syst
em a
nd o
ther
sys
tem
-spe
cific
re
sour
ces.
/ MAR
TE T
utor
ial
118
Prin
cipl
es o
f the
mod
el tr
ansf
orm
atio
n
Mod
el tr
ansf
orm
atio
n to
olki
tAT
Lht
tp://
ww
w.e
clip
se.o
rg/m
2m/a
tl/A
RIN
C-6
53 s
peci
ficm
odel
usi
ngS
RM
OS
EK
/VD
X S
peci
ficm
odel
usi
ngS
RM
Pat
tern
mat
chin
gde
scrip
tion
•Mat
chin
gru
les
•Des
ign
patte
rn d
escr
iptio
n
/ MAR
TE T
utor
ial
119
Mat
chin
gpa
ttern
exa
mpl
e(1
/2)
«Sw
Sche
dula
bleR
esou
rce
»B
asic
Task
acqu
isiti
on :
Bas
icTa
sk
traje
ctor
yCon
trolle
r : B
asic
Task
«in
stan
ceO
f»
«in
stan
ceO
f»
For e
ach
UM
L In
stan
ceS
peci
ficat
ion
{if
itscl
assi
fier h
as th
e S
RM
Sw
Sch
edul
able
Res
ourc
est
ereo
type
then
{•
gene
rate
a ne
w In
stan
ce S
peci
ficat
ion;
•its
targ
etcl
assi
fier i
sth
atw
hich
isst
ereo
type
dSw
Sche
dula
bleR
esou
rce
in th
e ta
rget
exe
cutio
n su
ppor
t;}e
ndG
ener
ate
}end
if}
«Sw
Sche
dula
bleR
esou
rce
»Pr
oces
s
acqu
isiti
on :
Pro
cess
traje
ctor
yCon
trolle
r : P
roce
ss
«in
stan
ceO
f»
«in
stan
ceO
f»
ATL
/ MAR
TE T
utor
ial
120
Mat
chin
gpa
ttern
exa
mpl
e(2
/2)
For e
ach
UM
L In
stan
ceS
peci
ficat
ion
{if
itscl
assi
fier h
as th
e S
RM
Sw
Sch
edul
able
Res
ourc
est
ereo
type
then
{•
…•e
ach
sour
ce p
riorit
yEle
men
tsm
atch
one
targ
etpr
iorit
yEle
men
ts}e
ndG
ener
ate
}end
if}
OS
EK
/VD
XA
RIN
C-6
53
ATL
/ MAR
TE T
utor
ial
121
Ass
ist u
ser t
o po
rt m
ulti-
task
des
igns
in th
e P
apyr
us U
ML
tool
: a
basi
c ex
ampl
e
/ MAR
TE T
utor
ial
122
Har
dwar
e R
esou
rce
Mod
elin
g w
ith M
AR
TE
�S
peci
aliz
atio
n of
GR
M�
HR
M a
nd S
RM
sha
re a
com
mon
stru
ctur
e�
It ea
ses
HW
/SW
allo
catio
n!
/ MAR
TE T
utor
ial
123
HR
M u
se c
ases
Softw
are
deve
lopp
er
MA
RTE
Syst
em A
rchi
tect
Ana
lyze
r
Ana
lysi
s
Allo
catio
nH
RM
Det
aile
d H
Wm
odel
ing
Spec
ializ
ed H
W
mod
elin
g
Hig
h le
vel H
W
mod
elin
g
HW
des
igne
r
App
licat
ion
mod
elin
g
«in
clud
e»
«ex
tend
»
«in
clud
e»
«in
clud
e»
«in
clud
e»
3 us
e ca
ses
= 3
leve
ls o
f det
ails
/ MAR
TE T
utor
ial
124
HR
M u
se c
ases
--H
igh
leve
l har
dwar
e m
odel
ing
�H
ow?
�H
igh
leve
l of a
bstr
actio
n�
Arc
hite
ctur
alvi
ew o
f the
HW
pla
tform
�W
ith k
ey p
rope
rties
:�
E.g
., in
stru
ctio
n se
t and
mem
ory
size
.�
A fo
rmal
vie
w o
f usu
al b
lock
dia
gram
s�
For
�H
igh
leve
l des
crip
tion
of e
xist
ing
and
targ
eted
HW
pla
tform
�Fi
rst s
teps
of d
esig
n of
new
HW
arc
hite
ctur
e�
By �S
yste
m a
rchi
tect
s�
Sof
twar
e de
velo
pers
/ MAR
TE T
utor
ial
125
HR
M u
se c
ases
--S
peci
aliz
ed h
ardw
are
mod
elin
g
�H
ow?
�S
peci
aliz
ed H
W d
escr
iptio
nm
odel
�N
atur
e of
det
ails
dep
ends
on
the
poin
t of v
iew
�E
x1 :
auto
nom
y an
alys
is re
quire
s po
wer
con
sum
ptio
n m
odel
ing
�E
x2 :
WC
ET
anal
ysis
nee
d de
tails
on
proc
esso
r spe
ed,
com
mun
icat
ion
band
wid
th a
nd m
emor
y or
gani
zatio
n…
�Fo
r ana
lysi
s pu
rpos
e�
By
anal
yzer
s
/ MAR
TE T
utor
ial
126
HR
M u
se c
ases
--D
etai
led
hard
war
e m
odel
ing
�H
ow?
�H
RM
is a
det
aile
d H
W a
rchi
tect
ure
desi
gn la
ngua
ge�
Leve
l of d
etai
ls d
epen
ds o
n th
e de
scrip
tion
accu
racy
�E
x1: F
unct
iona
l sim
ulat
or o
f a p
roce
ssor
onl
y re
quire
s its
inst
ruct
ion
set f
amily
�E
x2: P
erfo
rman
ce s
imul
atio
n ne
ed a
fine
des
crip
tion
of p
roce
ssor
s m
icro
-arc
hite
ctur
e.
�Fo
r�
Mod
el-b
ased
dat
ashe
ets
desc
riptio
n�
Sim
ulat
ion
�ge
nera
tion
of c
onfig
urat
ions
for s
imul
atio
n to
ols
�By �
HW
des
igne
rs
/ MAR
TE T
utor
ial
127
HR
M s
truct
ure
�H
iera
rchi
cal t
axon
omy
of h
ardw
are
conc
epts
�S
ucce
ssiv
e in
herit
ance
laye
rs�
From
gene
ric c
once
pts
(GR
M-li
ke)
�H
wC
ompu
tingR
esou
rce,
Hw
Mem
ory,
Hw
Com
mun
icat
ionR
esou
rce…
�To
spec
ific
and
deta
iled
reso
urce
s�
Hw
Pro
cess
or, H
wB
ranc
hPre
dict
or, H
wC
ache
, Hw
MM
U, H
wB
us,
Hw
Brid
ge, H
wD
MA
…�
All
HR
M c
once
pts
are
Hw
Res
ourc
e(s)
�Tw
om
odel
ing
view
sto
sep
arat
e co
ncer
ns
Logi
cal /
Phy
sica
l
/ MAR
TE T
utor
ial
128
�P
rovi
des
a fu
nctio
nald
escr
iptio
n�
Bas
ed o
n a
func
tiona
l cla
ssifi
catio
n of
har
dwar
e re
sour
ces:
HR
M s
truct
ure
--Lo
gica
l mod
elin
g
/ MAR
TE T
utor
ial
129
HR
M s
truct
ure
--P
hysi
cal m
odel
ing
�P
rovi
des
a ph
ysic
alpr
oper
ties
desc
riptio
n�
Bas
ed o
n bo
th fo
llow
ing
pack
ages
�H
wLa
yout
�Fo
rms:
Chi
p, C
ard,
Cha
nnel
…�
Dim
ensi
ons,
are
a an
d ar
rang
emen
t mec
hani
sm w
ithin
rect
ilinea
r grid
s�
Env
ironm
enta
l con
ditio
ns: e
.g. t
empe
ratu
re, v
ibra
tion,
hum
idity
…�
Hw
Pow
er�
Pow
er c
onsu
mpt
ion
and
heat
dis
sipa
tion
/ MAR
TE T
utor
ial
130
HR
M p
rofil
e ov
ervi
ew
/ MAR
TE T
utor
ial
131
mem
oryS
ize
: NFP
_Dat
aSiz
ead
dres
sSiz
e : N
FP_D
ataS
ize
timin
gs :
Tim
ing
[*]
«st
ereo
type
»H
wM
emor
y
«st
ereo
type
»M
AR
TE::G
RM
::Sto
rage
«st
ereo
type
»H
wR
esou
rce
leve
l : N
FP_N
atur
al =
1ty
pe :
Cac
heTy
pest
ruct
ure
: Cac
heSt
ruct
ure
repl
_Pol
icy
: Rep
l_P
olic
yw
riteP
olic
y : W
riteP
olic
y
«st
ereo
type
»H
wC
ache
orga
niza
tion
: Mem
oryO
rgan
izat
ion
isSy
nchr
onou
s : N
FP_B
oole
anis
Stat
ic :N
FP_B
oole
anis
Non
Vola
tile
: NFP
_Boo
lean
repl
_Pol
icy
: Rep
l_Po
licy
writ
ePol
icy
: Writ
ePol
icy
«st
ereo
type
»H
wR
AM
Dat
aIn
stru
ctio
nU
nifie
dO
ther
Und
efin
ed
«en
umer
atio
n»
Cac
heTy
pe
nota
tion
: NFP
_Stri
ngde
scrip
tion
: NFP
_Stri
ngva
lue
: NFP
_Dur
atio
n
«da
taTy
pe»
Tim
ing
nbR
ows
: NFP
_Nat
ural
nbC
olum
ns :
NFP
_Nat
ural
nbBa
nks
: NFP
_Nat
ural
wor
dSiz
e : N
FP_D
ataS
ize
«da
taTy
pe»
Mem
oryO
rgan
izat
ion
nbSe
ts :
NFP
_Nat
ural
bloc
Size
: N
FP_D
ataS
ize
asso
ciat
ivity
: N
FP_N
atur
al
«da
taTy
pe»
Cac
heSt
ruct
ure
Writ
eBac
kW
riteT
hrou
ghO
ther
Und
efin
ed
«en
umer
atio
n»
Writ
ePol
icy
LRU
NFU
FIFO
Ran
dom
Oth
erU
ndef
ined
«en
umer
atio
n»
Rep
l_Po
licy
type
: R
OM
_Typ
eor
gani
zatio
n : M
emor
yOrg
aniz
atio
n
«st
ereo
type
»H
wR
OM
Mas
kedR
OM
EPR
OM
OTP
_EPR
OM
EEPR
OM
Flas
hO
ther
Und
efin
ed
«en
umer
atio
n»
RO
M_T
ype
sect
orSi
ze :
NFP
_Dat
aSiz
e
«st
ereo
type
»H
wD
rive
buffe
r{s
ubse
ts o
wne
dHW
}
0..1
Hw
Mem
ory
HR
M p
rofil
e --
Hw
Mem
ory
/ MAR
TE T
utor
ial
132
mem
oryS
ize
: NFP
_Dat
aSiz
ead
dres
sSiz
e : N
FP_D
ataS
ize
timin
gs :
Tim
ing
[*]
«st
ereo
type
»H
wM
emor
y
«st
ereo
type
»M
AR
TE::G
RM
::Sto
rage
«st
ereo
type
»H
wR
esou
rce
orga
niza
tion
: Mem
oryO
rgan
izat
ion
isSy
nchr
onou
s : N
FP_B
oole
anis
Stat
ic :N
FP_B
oole
anis
Non
Vola
tile
: NFP
_Boo
lean
repl
_Pol
icy
: Rep
l_Po
licy
writ
ePol
icy
: Writ
ePol
icy
«st
ereo
type
»H
wR
AM
Dat
aIn
stru
ctio
nU
nifie
dO
ther
Und
efin
ed
«en
umer
atio
n»
Cac
heTy
pe
nota
tion
: NFP
_Stri
ngde
scrip
tion
: NFP
_Stri
ngva
lue
: NFP
_Dur
atio
n
«da
taTy
pe»
Tim
ing
nbR
ows
: NFP
_Nat
ural
nbC
olum
ns :
NFP
_Nat
ural
nbBa
nks
: NFP
_Nat
ural
wor
dSiz
e : N
FP_D
ataS
ize
«da
taTy
pe»
Mem
oryO
rgan
izat
ion
nbSe
ts :
NFP
_Nat
ural
bloc
Size
: N
FP_D
ataS
ize
asso
ciat
ivity
: N
FP_N
atur
al
«da
taTy
pe»
Cac
heSt
ruct
ure
Writ
eBac
kW
riteT
hrou
ghO
ther
Und
efin
ed
«en
umer
atio
n»
Writ
ePol
icy
LRU
NFU
FIFO
Ran
dom
Oth
erU
ndef
ined
«en
umer
atio
n»
Rep
l_Po
licy
type
: R
OM
_Typ
eor
gani
zatio
n : M
emor
yOrg
aniz
atio
n
«st
ereo
type
»H
wR
OM
Mas
kedR
OM
EPR
OM
OTP
_EPR
OM
EEPR
OM
Flas
hO
ther
Und
efin
ed
«en
umer
atio
n»
RO
M_T
ype
sect
orSi
ze :
NFP
_Dat
aSiz
e
«st
ereo
type
»H
wD
rive
buffe
r{s
ubse
ts o
wne
dHW
}
0..1
Hw
Mem
ory
HR
M p
rofil
e --
Hw
Mem
ory
--H
wC
ache
�H
wC
ache
is a
pro
cess
ing
mem
ory
whe
re
frequ
ently
use
d da
ta c
an b
e st
ored
for
rapi
d ac
cess
.�
Det
aile
d de
scrip
tion
of th
e H
wC
ache
is
nece
ssar
y fo
r per
form
ance
ana
lysi
s an
d si
mul
atio
n
/ MAR
TE T
utor
ial
133
HR
M p
rofil
e --
Hw
Mem
ory
--H
wC
ache
�S
peci
fies
the
cach
e le
vel.
�D
efau
lt va
lue
is 1
/ MAR
TE T
utor
ial
134
HR
M p
rofil
e --
Hw
Mem
ory
--H
wC
ache
�S
peci
fies
the
Hw
Cac
hest
ruct
ure
�H
wC
ache
is o
rgan
ized
und
er s
ets
of b
lock
s.�
Ass
ocia
tivity
is th
e nu
mbe
r of b
lock
s w
ithin
ea
ch s
et.
�If
asso
ciat
ivity
= 1,
cac
he is
dire
ct m
appe
d.�
If nb
Set
s=
1, c
ache
is fu
lly a
ssoc
iativ
e.�
OC
L ru
le:
mem
oryS
ize
= nb
Set
s x
bloc
Siz
e x
asso
ciat
ivity
/ MAR
TE T
utor
ial
135
HR
M p
rofil
e --
Hw
Mem
ory
--H
wC
ache
�S
peci
fies
the
cach
e w
rite
polic
y �
Writ
eBac
k: C
ache
writ
e is
not
imm
edia
tely
re
flect
ed to
the
back
ing
mem
ory.
�W
riteT
hrou
gh: W
rites
are
imm
edia
tely
m
irror
ed.
/ MAR
TE T
utor
ial
136
�H
RM
ste
reot
ypes
ext
ends
the
mai
n st
ruct
ural
UM
L m
etac
lass
es�
Cla
ssifi
er, C
lass
�In
stan
ceS
peci
ficat
ion,
Pro
perty
�A
ssoc
iatio
n (H
wM
edia
, Hw
Bus
…),
Por
t (H
wE
ndP
oint
)
�H
RM
can
be
used
with
all
Stru
ctur
al U
ML
diag
ram
s:�
Cla
ss d
iagr
am�
Com
pone
nt d
iagr
am�
Com
posi
te S
truct
ure
Dia
gram
(wel
l ada
pted
for H
W)
�H
RM
pro
file
appl
icat
ion
�Ta
g de
finiti
ons
are
optio
nal
�Sp
ecifi
ed if
need
ed�
Spec
ified
whe
nne
eded
(Ref
inem
ent)
�A
t cla
ss le
vel f
or te
chno
logy
def
initi
on (e
.g. t
ype
of H
wC
ache
)�
At i
nsta
nce
leve
l for
com
pone
nt d
efin
ition
(e.g
. siz
e of
Hw
Cac
he)
HR
M u
sage
/ MAR
TE T
utor
ial
137
Ver
y ea
rly H
w A
rchi
tect
ure
Des
crip
tion
�S
MP
(Sym
met
ric M
ultiP
roce
ssin
g) h
ardw
are
plat
form
�4
iden
tical
pro
cess
ors
�U
nifie
d Le
vel 2
cac
he fo
r eac
h�
Sha
red
mai
n m
emor
y (S
DR
AM
)�
Cen
tral F
SB
(Fro
nt S
ide
Bus
)�
DM
A (D
irect
Mem
ory
Acc
ess)
�B
atte
ryO
nly
Hw
Res
ouce
s
/ MAR
TE T
utor
ial
138
HR
M u
sage
exa
mpl
e: L
ogic
al v
iew
«hw
Logi
cal::
hwR
esou
rce
»sm
p : S
MP
«hw
Proc
esso
r»cp
u1 :
CPU
{freq
uenc
y =
800M
hz}
«hw
Cac
he»
l2 :
UL2
{mem
oryS
ize
= 51
2kB
}
«hw
RA
M»
sdra
m :
SDR
AM
{freq
uenc
y =
266M
hz,
mem
oryS
ize
= 25
6MB
}
«hw
Supp
ort»
batte
ry :
Bat
tery
«hw
DM
A»
dma
: DM
A{m
anag
edM
emor
ies
= sd
ram
}
«hw
Proc
esso
r»cp
u2 :
CPU
{freq
uenc
y =
800M
hz}
«hw
Cac
he»
l2 :
UL2
{mem
oryS
ize
= 51
2kB
}
«hw
Proc
esso
r»cp
u3 :
CPU
{freq
uenc
y =
800M
hz}
«hw
Cac
he»
l2 :
UL2
{mem
oryS
ize
= 51
2kB
}
«hw
Proc
esso
r»cp
u4 :
CPU
{freq
uenc
y =
800M
hz}
«hw
Cac
he»
l2 :
UL2
{mem
oryS
ize
= 51
2kB
}
«hw
Bus
»fs
b : F
SB{fr
eque
ncy
= 13
3Mhz
,w
ordW
idth
= 1
28bi
t}
/ MAR
TE T
utor
ial
139
HR
M u
sage
exa
mpl
e: P
hysi
cal v
iew
«hw
Car
d»
smp
: SM
Pgr
id =
4,3
area
= 5
000m
m²
r_co
nditi
ons
= (T
empe
ratu
re; O
pera
ting;
‘’’’;
[10°
C,6
0°C
])
«hw
Chi
p»
cpu1
: C
PU
posi
tion
= [1
,1],
[1,1
]st
atic
Con
sum
ptio
n =
5W
«hw
Chi
p»
cpu3
: C
PU
posi
tion
= [2
,2],
[1,1
]st
atic
Con
sum
ptio
n =
5W
«hw
Chi
p»
cpu4
: C
PU
posi
tion
= [2
,2],
[3,3
]st
atic
Con
sum
ptio
n =
5W
«hw
Chi
p»
cpu2
: C
PU
posi
tion
= [1
,1],
[3,3
]st
atic
Con
sum
ptio
n =
5W
«hw
Chi
p»
dma
: DM
A
posi
tion
= [3
,3],
[3,3
]
«hw
Pow
erSu
pply
»ba
ttery
: B
atte
ry
posi
tion
= [4
,4],
[3,3
]ca
paci
ty =
10W
hw
eigh
t = 1
50g
«hw
Car
d»
sdra
m :
SDR
AM po
sitio
n =
[3,4
], [1
,1]
nbP
ins
= 14
4
«hw
Cha
nnel
»fs
b : F
SBpo
sitio
n =
[1,4
], [2
,2]
«hw
Com
pone
nt»
SMP
{kin
d =
Car
d}
«hw
Com
pone
nt»
CPU
[4] {k
ind
= C
hip}
«hw
Com
pone
nt»
UL2 {kin
d =
Uni
t}
«hw
Com
pone
nt»
FSB {kin
d =
Cha
nnel
}
«hw
Com
pone
nt»
SDR
AM {k
ind
= C
ard}
«hw
Pow
erSu
pply
»B
atte
ry {kin
d =
Oth
er,
capa
city
= 4
0Wh}
«hw
Com
pone
nt»
DM
A {kin
d =
Chi
p}
/ MAR
TE T
utor
ial
140
HR
M c
ase
stud
y --
TC17
96 (μ
Con
trolle
r)
�A
dvan
ced
32-b
it Tr
iCor
e™-b
ased
Nex
t Gen
erat
ion
Mic
roco
ntro
ller f
or R
eal-T
ime
Em
bedd
ed s
yste
ms
�A
utom
otiv
e co
ntro
l sys
tem
s�
Indu
stria
l rob
otic
con
trol
�Fe
atur
es�
Sup
er-s
cala
r TriC
ore
CP
U�
Sup
erio
r rea
l-tim
e pe
rform
ance
�E
ffici
ent i
nter
rupt
han
dlin
g�
4 st
age
pipe
line
�D
SP
cap
abili
ties
�15
0 M
Hz
oper
atio
nal f
requ
ency
/ MAR
TE T
utor
ial
141
HR
M c
ase
stud
y --
TC17
96
�C
ompl
ex m
emor
y ar
chite
ctur
e�
Em
bedd
ed P
rogr
am M
emor
y (>
2MB
yte)
: PM
I (IC
AC
HE
, S
PR
AM
), P
MU
(BR
OM
, PFL
AS
H, D
FLA
SH
)�
Dat
a M
emor
y : D
MI(L
DR
AM
, DP
RA
M),
DM
U(S
RA
M, S
BR
AM
)…�
Ext
enda
ble
mem
ory
usin
g an
ext
erna
l bus
�H
igh
perfo
rman
ce tr
iple
bus
stru
ctur
e�
Two
Loca
l mem
ory
buss
es (6
4-bi
t) to
pro
gram
and
dat
a m
emor
ies
�32
-bit
syst
em p
erip
hera
l bus
to o
n-ch
ip p
erip
hera
ls�
32-b
it re
mot
e pe
riphe
ral b
us to
ext
erna
l per
iphe
rals
�In
depe
nden
t bus
con
trol u
nits
�16
-cha
nnel
DM
A c
ontro
ller… / M
ARTE
Tut
oria
l14
2
Blo
ck d
iagr
am o
f the
TC
1796
CP
U-S
ubsy
stem
/ MAR
TE T
utor
ial
143
HR
M c
ase
stud
y --
TC17
96 C
PU
-Sub
syst
em
DEM
O(P
apyr
us)
/ MAR
TE T
utor
ial
144
HR
M a
pplic
atio
n --
HW
em
ulat
ion
�U
ML
mod
els
have
now
a p
reci
se s
tand
ard
XM
L re
pres
enta
tion
(usi
ng th
e X
MI d
efin
ition
).�
Then
, all
mod
el m
anip
ulat
ions
and
tran
sfor
mat
ions
can
be
easi
ly d
one
usin
g w
idel
y kn
own
XM
L te
chno
logi
es.
�E
clip
se p
lugi
ns(E
MF,
UM
L2…
), A
ccel
eo…
�Th
e st
eps
are:
1.D
escr
ibe
the
HW
mod
els
in U
ML
usin
g H
RM
2.
Pars
ean
d C
aptu
re a
ll th
e re
quire
d H
W p
rope
rties
3.Ve
rify
cohe
renc
y an
d co
mpl
etio
n4.
Gen
erat
eth
e co
nfig
urat
ion
file
for t
he ta
rget
em
ulat
ion
tool
5.Si
mul
ate
the
appl
icat
ion
softw
are
on th
e em
ulat
ed H
W
/ MAR
TE T
utor
ial
145
Exa
mpl
es o
f Pos
sibl
e H
w E
mul
ator
s�
Sim
ics
(Virt
utec
h, w
ww
.virt
utec
h.co
m/)
�S
uppo
rt fo
r mos
t HW
com
pone
nts
�Fu
nctio
nal a
nd P
erfo
rman
ce s
imul
atio
n�
Ena
ble
to ru
n he
avy
softw
are
appl
icat
ions
(e.g
., lin
ux)
�Fr
ee fo
r aca
dem
ics
�S
kyey
e(w
ww
.sky
eye.
org/
)�
Sup
port
for A
RM
-like
pro
cess
ors,
mos
t of m
emor
ies
and
perip
hera
ls�
Func
tiona
l sim
ulat
ion
�E
nabl
e to
run
only
ligh
t sw
appl
icat
ions
(E.g
., μL
inux
and
AR
MLi
nux)
�G
PL
�S
impl
eSca
lar(
ww
w.s
impl
esca
lar.c
om/)
�A
cade
mic
tool
eas
y to
ext
end
�P
erfo
rman
ce s
imul
atio
n�
Run
C c
ode
/ MAR
TE T
utor
ial
146
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
147
Des
ign
ratio
nale
for G
QA
M
�U
pdat
es S
PT
�A
lignm
ent t
o U
ML2
�H
arm
oniz
atio
n be
twee
n th
e tw
o S
PT
anal
ysis
sub
-pro
files
: sc
hedu
labi
lity
and
perfo
rman
ce�
Ext
ensi
on o
f tim
ing
anno
tatio
ns e
xpre
ssiv
enes
s�
Ove
rhea
ds (e
.g. m
essa
ges
pass
ing)
�R
espo
nse
times
(e.g
. BC
ET
& A
CE
T)�
Tim
ing
requ
irem
ents
(e.g
. mis
s ra
tios
and
max
. jitt
ers)
�P
rovi
des
supp
ort f
or m
etho
dolo
gica
l asp
ects
�S
ensi
tivity
ana
lysi
s an
d de
sign
-spa
ce e
xplo
ratio
n�
Mod
elin
g re
use
and
com
pone
nt-b
ased
des
ign
�S
uppo
rt of
the
MD
A a
ppro
ach
/ MAR
TE T
utor
ial
148
GQ
AM
ove
rvie
w�
Gen
eric
Qua
ntita
tive
Ana
lysi
sM
odel
ing
(GQ
AM
) exp
ress
es
foun
datio
n co
ncep
tsan
d N
FPs
shar
ed b
y d
iffer
ent q
uant
itativ
e an
alys
is d
omai
ns –
e.g.
, sch
edul
abili
tyan
d pe
rform
ance
-th
at h
ave
thei
r ow
n te
rmin
olog
y, c
once
pts
and
sem
antic
s�
core
GQ
AM
con
cept
s de
scrib
e ho
w th
e sy
stem
beh
avio
r use
s re
sour
ces
over
tim
e�
NFP
sus
ed in
qua
ntita
tive
anal
ysis
tech
niqu
es:
�“in
put N
FPs”
-tak
en a
s in
put d
ata
�e.
g., r
eque
st o
r trig
ger a
rriva
l rat
es, e
xecu
tion
dem
ands
, dea
dlin
es, Q
oSta
rget
s�
“out
put N
FPs”
-com
pute
d as
resu
lts�
e.g.
, res
pons
e tim
es, d
eadl
ine
failu
res,
reso
urce
util
izat
ions
, que
ue s
izes
�D
iffer
ent a
naly
sis
goal
s:
�P
oint
eva
luat
ion
of th
e ou
tput
NFP
sfo
r a g
iven
ope
ratin
g po
int d
efin
ed
by in
put N
FPs
�S
earc
h ov
er th
e pa
ram
eter
spa
ce fo
r fea
sibl
e or
opt
imal
sol
utio
ns�
Sen
sitiv
ity o
f som
e ou
tput
resu
lts to
som
e in
put p
aram
eter
s�
Sca
labi
lity
anal
ysis
: how
the
syst
em p
erfo
rms
whe
n th
e pr
oble
m s
ize
or
the
syst
em s
ize
grow
.
/ MAR
TE T
utor
ial
149
Pro
cess
ing
sche
ma
for m
odel
-bas
edan
alys
is
Ana
lysi
s sp
ecifi
c fr
amew
ork
UM
L2 +
Mar
te
UM
L2 e
dito
r
Ann
otat
edm
odel
«pr
ofile
»
MA
RTE
Res
ults
/Dia
gnos
ticA
naly
sis
resu
lts
Ana
lysi
sto
ol
Anal
ysis
mod
elM
odel
conv
erte
r
Res
ults
conv
erte
r
/ MAR
TE T
utor
ial
150
GQ
AM
dep
ende
ncie
s an
d ar
chite
ctur
e�
GQ
AM
(Gen
eric
Qua
ntita
tive
Ana
lysi
sM
odel
ing)
:Com
mon
con
cept
s fo
r ana
lysi
s�
SAM
:Mod
elin
g su
ppor
t for
sch
edul
abili
tyan
alys
is te
chni
ques
.�
PAM
:Mod
elin
g su
ppor
t for
per
form
ance
ana
lysi
s te
chni
ques
.
/ MAR
TE T
utor
ial
151
GQ
AM
dom
ain
mod
el –
top
pack
age
�M
ain
conc
epts
com
mon
for q
uant
itativ
e an
alys
is:
�R
esou
rces
�Be
havi
or�
Wor
kloa
d�
All
embe
dded
in a
n an
alys
is c
onte
xt (m
ay h
ave
anal
ysis
par
amet
ers)
/ MAR
TE T
utor
ial
152
GQ
AM
dom
ain
mod
el: W
orkl
oad
and
Beh
avio
rW
orkl
oad
Beh
avio
r/Sc
enar
io
Step
s
/ MAR
TE T
utor
ial
153
Wor
kloa
d co
ncep
ts
�D
iffer
ent w
orkl
oads
corr
espo
nd to
diff
eren
t ope
ratin
g m
odes
:�
take
off,
in-fl
ight
and
land
ing
of a
n ai
rcra
ft�
peak
-load
and
ave
rage
-load
of a
n en
terp
rise
appl
icat
ion.
�
A w
orkl
oad
is re
pres
ente
d by
a s
tream
of t
rigge
ring
even
ts,
Wor
kloa
dEve
nt, g
ener
ated
in o
ne o
f the
follo
win
g w
ays:
�
by a
tim
ed e
vent
�by
a g
iven
arr
ival
pat
tern
�pe
riodi
c, a
perio
dic,
spo
radi
c, b
urst
, irr
egul
ar, o
pen,
clo
sed
�by
a g
ener
atin
g m
echa
nism
nam
ed W
orkl
oad
Gen
erat
or(m
ay b
e m
odel
ed a
s a
stat
e-m
achi
ne)
�m
ultip
le in
depe
nden
t ide
ntic
al g
ener
atin
g m
echa
nism
s m
ay e
xist
(th
eir n
umbe
r is
calle
d its
"pop
ulat
ion“
) �
from
a tr
ace
(Eve
ntTr
ace)
sto
red
in a
file
.
/ MAR
TE T
utor
ial
154
Beh
avio
r sce
nario
con
cept
s�
Beh
avio
rSce
nario
desc
ribes
a b
ehav
ior t
rigge
red
by a
n ev
ent
�co
mpo
sed
of s
ub-o
pera
tions
cal
led
Ste
ps�
any
Ste
p m
ay b
e re
fined
as
anot
her B
ehav
iorS
cena
rio�
A B
ehav
iorS
cena
rioca
ptur
es a
ny s
yste
m-le
vel b
ehav
ior d
escr
iptio
n or
any
op
erat
ion
and
atta
ches
reso
urce
usa
ge to
it in
diff
eren
t way
s:�
each
prim
itive
Ste
p ex
ecut
es o
n a
host
pro
cess
or�
a S
tep
impl
icitl
y us
es a
Sch
edul
able
Res
ourc
e(p
roce
ss, t
hrea
d or
task
) �
a S
tep
may
be
a sp
ecia
lized
Acq
uire
Step
or R
elea
seSt
epto
acq
uire
or r
elea
se a
R
esou
rce
�B
ehav
iorS
cena
rios
and
Ste
ps m
ay u
se o
ther
kin
d of
reso
urce
s, s
o B
ehav
iorS
cena
rioin
herit
s fro
m R
esou
rceU
sage
whi
ch li
nks
reso
urce
s w
ith
conc
rete
usa
ge d
eman
ds.
�a
few
con
cret
e fo
rms
of u
sage
def
ined
in G
QA
M: m
emor
y, C
PU
exe
cutio
n tim
e, e
nerg
y fro
m a
pow
er s
uppl
y an
d si
ze o
f mes
sage
s.
�P
rede
cess
or-s
ucce
ssor
rela
tions
hip
betw
een
Ste
ps:
�se
quen
ce, b
ranc
h, m
erge
, for
k, jo
in�
A C
omm
unic
atio
nSte
pde
fines
the
conv
eyan
ce o
f a m
essa
ge�
Ser
vice
sar
e pr
ovid
ed b
y re
sour
ces
and
by s
ubsy
stem
s�
a su
bsys
tem
ser
vice
ass
ocia
ted
with
an
inte
rface
ope
ratio
n p
rovi
ded
by a
co
mpo
nent
may
be
iden
tifie
d as
a R
eque
sted
Ser
vice
�R
eque
sted
Ser
vice
is a
sub
type
of S
tep
-may
be
refin
ed b
y a
Beh
avio
rSce
nario
.
/ MAR
TE T
utor
ial
155
GQ
AM
dom
ain
mod
el: R
esou
rces
Phys
ical
Res
ourc
esLo
gica
lR
esou
rces
/ MAR
TE T
utor
ial
156
Res
ourc
e co
ncep
ts�
Res
ourc
esP
latfo
rm-t
he to
p cl
ass
in th
e G
QA
M_R
esou
rce
pack
age
repr
esen
ts a
logi
cal c
onta
iner
for a
ll th
e re
sour
ces
�Th
e vi
ewpo
int o
f res
ourc
es is
bas
ed o
n th
e ab
stra
ct R
esou
rce
clas
s fro
m th
e G
RM
pac
kage
�co
mm
on fe
atur
es in
clud
e a
sche
dulin
g di
scip
line,
mul
tiplic
ity, s
ervi
ces
�Fr
om a
n an
alys
is v
iew
poin
t, fo
ur ty
pes
of re
sour
ces
are
impo
rtant
:�
Exe
cutio
nHos
t: a
proc
esso
r or o
ther
dev
ice
that
exe
cute
s op
erat
ions
sp
ecifi
ed in
the
mod
el. I
t has
a h
ost r
ole
rela
tive
to th
e pr
oces
ses
and
the
Ste
ps th
at e
xecu
te o
n it.
�C
omm
unic
atio
nsH
ost:
hard
war
e lin
ks b
etw
een
devi
ces,
with
the
role
of
host
to th
e co
nvey
ance
of a
mes
sage
.�
Sch
edul
able
Res
ourc
e: a
sch
edul
able
ser
vice
like
a p
roce
ss o
r thr
ead
pool
, whi
ch is
a s
oftw
are
reso
urce
man
aged
by
the
OS
.�
Com
mun
icat
ionC
hann
el: a
mid
dlew
are
or p
roto
col l
ayer
that
con
veys
m
essa
ges.
�
Ther
e ar
e al
so o
ther
con
curre
ncy
reso
urce
s, s
uch
as m
utua
l ex
clus
ion
reso
urce
s fro
m th
e G
RM
cha
pter
�e.
g., c
ritic
al s
ectio
n; s
emap
hore
s an
d lo
cks;
a fi
nite
buf
fer p
ool;
pool
of
adm
issi
on c
ontro
l tok
ens.
/ MAR
TE T
utor
ial
157
GQ
AM
dom
ain
mod
el: O
bser
vers
�Ti
min
g O
bser
vers
are
conc
eptu
al e
ntiti
es th
at c
olle
ct ti
min
g re
quire
men
ts a
nd p
redi
ctio
ns re
late
d to
a p
air o
f use
r-de
fined
obs
erve
d ev
ents
sta
rtObs
and
endO
bs.
�an
nota
te a
nd c
ompa
re ti
min
g co
nstra
ints
aga
inst
tim
ing
pred
ictio
ns p
rovi
ded
by a
naly
sis
tool
s �
can
be u
sed
as p
rede
fined
and
par
amet
eriz
ed p
atte
rns
or b
y m
eans
of m
ore
elab
orat
e ex
pres
sion
s (e
.g.,
in O
CL
or V
SL)
�
Late
ncyO
bser
vers
peci
fies
a du
ratio
n ob
serv
atio
n, w
ith a
mis
s ra
tio a
sser
tion
(per
cent
age)
, a
utili
ty fu
nctio
n pl
acin
g a
valu
e on
the
dura
tion,
and
a ji
tter c
onst
rain
t.
/ MAR
TE T
utor
ial
158
Com
mon
NFP
Attr
ibut
es fo
r Ana
lysi
s (1
)N
FPFo
r R
esou
rce
For
Scen
ario
an
d St
epFo
r W
orkl
oad
Even
tre
petit
ions
: NFP
_Rea
l[*]
N/A
the nu
mber
of tim
es th
e Step
is
repe
ated,
once
trigg
ered
(defa
ult =
1).
N/A
prob
abilit
y: N
FP_R
eal[*
]N/
Athe
prob
abilit
y tha
t the s
tep is
exec
uted,
follow
ing its
pred
eces
sor (
for
cond
itions
)
N/A
host
Dem
and:
NFP_
Dura
tion[
*],ho
stDe
man
dOps
:NFP
_Rea
l[*]
comp
osite
dema
nd ac
ross
all
servi
ces o
f the
Reso
urce
, in te
rms o
f tim
e and
in te
rms o
f pr
oces
sor o
pera
tions
For a
Step
, the C
PU de
mand
on th
e ho
st of
the pr
oces
s tha
t exe
cutes
the
Step
.Fo
r a S
cena
rio, th
e sum
of al
l dem
ands
for
all it
s Step
s.
N/A
prio
rity :
NFP
_Inte
ger[*
]N/
AFo
r a S
tep, p
riority
on its
host
N/A
resp
Tim
e: N
FP_D
urat
ion[
*]re
spon
se tim
e,co
mpos
ite av
erag
e re
spon
se tim
e acro
ss
all se
rvice
s offe
red b
y the
reso
urce
total
delay
from
the t
rigge
r eve
nt un
til co
mplet
ion of
the S
tep or
Sc
enar
io
requ
ired v
alue f
or th
e Sc
enar
io
exec
Tim
e : N
FP_D
urat
ion[
*]ex
ecuti
on tim
ere
spTim
e minu
s any
sche
dulin
g dela
ysN/
A
/ MAR
TE T
utor
ial
159
Com
mon
NFP
Attr
ibut
es fo
r Ana
lysi
s (2
)N
FPFo
r R
esou
rce
For
Scen
ario
an
d St
epFo
r W
orkl
oad
Even
tin
terO
ccTi
me:
NFP
_Dur
atio
n[*]
inter
-occ
urre
nce t
ime,
inter
val b
etwee
n su
cces
sive r
eque
sts
for se
rvice
s
inter
val b
etwee
n init
iation
sint
erva
l betw
een
trigge
r eve
nts
thro
ughp
ut :
NFP_
Freq
uenc
y[*]
frequ
ency
of re
ques
ts for
all
servi
ces
frequ
ency
of in
itiatio
nsfre
quen
cy of
the
trigge
r eve
nt
utiliz
atio
n : N
FP_R
eal[*
]fra
ction
of tim
e the
re
sour
ce is
activ
e (h
as an
activ
e se
rvice
). Fo
r a
multip
le re
sour
ce,
the m
ean n
umbe
r of
busy
units
.
fracti
on of
time t
he B
ehav
iorSc
enar
io is
activ
e (be
twee
n its
trigge
r eve
nt an
d its
comp
letion
)
N/A
utiliz
atio
nOnH
ost:
NFP_
Real[
*]N/
Afra
ction
of tim
e the
host
is bu
sy
exec
uting
the B
ehav
iorSc
enar
io. If
it has
mult
iple h
osts,
this
is a s
et of
value
s.
N/A
bloc
kingT
ime:
NFP
_Dur
atio
n[*]
block
ing tim
ea p
ure d
elay w
hich i
s par
t of th
e be
havio
r of th
e Step
or S
cena
rioN/
A
/ MAR
TE T
utor
ial
160
GQ
AM
Pro
file:
top-
leve
l ste
reot
ypes
�Th
e U
ML
exte
nsio
ns fo
r the
GQ
AM
sub
-pro
file
are
pres
ente
d in
four
fig
ures
rela
ted
to c
orre
spon
ding
dom
ain
mod
el p
acka
ges:
�G
QA
M, G
QA
M_W
orkl
oad,
GQ
AM
_Res
ourc
esan
d G
QA
M_O
bser
vers
.
/ MAR
TE T
utor
ial
161
GQ
AM
Pro
file:
Wor
kloa
d an
d B
ehav
iour
Wor
kloa
d
Scen
ario
Step
s
/ MAR
TE T
utor
ial
162
App
lyin
g be
havi
or-re
late
d st
ereo
type
s
�G
aSce
nario
and
Ste
pst
ereo
type
s in
herit
from
:�
Tim
eMod
els:
:Tim
edP
roce
ssin
g-w
hich
in tu
rn e
xten
ds B
ehav
ior,
Mes
sage
, Act
ions
�an
d fro
mG
RM
::Res
ourc
eUsa
ge-w
hich
ext
ends
Nam
edE
lem
ent
�Th
eref
ore,
Sce
nario
and
Ste
p st
ereo
type
s ca
n be
app
lied
to a
w
ide
set o
f beh
avio
r-re
late
d el
emen
ts c
over
ed b
y th
e U
ML
2 m
etac
lass
Nam
edE
lem
ent,
such
as:
�O
pera
tions
, Act
ions
�M
essa
ges
that
initi
ate
Ope
ratio
nsor
Act
ions
�Tr
ansi
tions
and
Sta
tes
in s
tate
mac
hine
dia
gram
s�
Sig
nals
that
trig
ger s
tate
mac
hine
tran
sitio
ns�
Eve
nts,
Exe
cutio
nOcc
urre
nceS
peci
ficat
ions
and
Inte
ract
ionF
ragm
ents
in in
tera
ctio
n di
agra
ms
�In
putP
ins
in a
ctiv
ity d
iagr
ams
�U
seC
ases
.
/ MAR
TE T
utor
ial
163
«pr
ofile
»G
QA
M
«st
ereo
type
»M
AR
TE::
GR
M::
Res
ourc
e
«st
ereo
type
»M
AR
TE::
GR
M::
Proc
essi
ngR
esou
rce
«st
ereo
type
»M
AR
TE::
GR
M::
Con
curr
ency
Res
ourc
e
com
mTx
Ovh
: N
FP_
Dur
atio
nco
mm
Rcv
Ovh
: N
FP_
Dur
atio
ncn
txtS
wT:
NFP
_D
urat
ion
cloc
kOvh
: N
FP_
Dur
atio
nsc
hedP
riRan
ge:
NFP
_In
terv
alm
emSi
ze:
NFP
_D
ataS
ize
utiliz
atio
n: N
FP_
Rea
l [*]
thro
ughp
ut:
NFP
_Fr
eque
ncy
[*]
«st
ereo
type
»G
aExe
cHos
t
capa
city
: N
FP_
Dat
aTxR
ate
[*]pa
cket
T: N
FP_
Dur
atio
n [*]
bloc
kT:
NFP
_D
urat
ion
[*]
trans
mM
ode:
Tr
ansm
Mod
eKin
dut
ilizat
ion:
NFP
_R
eal [
*]th
roug
hput
: N
FP_
Freq
uenc
y [*]
«st
ereo
type
»G
aCom
mH
ost
«st
ereo
type
»M
AR
TE::
GR
M::
Sche
dula
bleR
esou
rce
pack
etSi
ze:
NFP
_D
ataS
ize
utiliz
atio
n: N
FP_
Rea
l [*]
«st
ereo
type
»G
aCom
mC
hann
el
Sim
ple
Hal
fDup
lex
FullD
uple
x
«en
umer
atio
n»
MA
RTE
_Li
brar
y::
MA
RTE
_D
ataT
ypes
::Tr
ansm
Mod
eKin
d
GQ
AM
Pro
file:
Res
ourc
es�
In g
ener
al, r
esou
rce-
rela
ted
ster
eoty
pes
exte
nd th
e U
ML
met
acla
sses
Cla
ssifi
er,
Inst
ance
Spe
cific
atio
nan
d P
rope
rty�
Ther
efor
e, re
sour
ce s
tere
otyp
es c
an b
e ap
plie
d to
all
kind
s of
cla
sses
, ins
tanc
es,
com
pone
nts,
par
ts a
nd d
eplo
ymen
t nod
es.
Logi
cal
reso
urce
s
Phys
ical
Res
ourc
es
/ MAR
TE T
utor
ial
164
GQ
AM
Pro
file:
Obs
erve
rs�
A ti
min
g ob
serv
er m
ay b
e at
tach
ed to
a s
tart
and
end
obse
rved
eve
nts,
or t
o a
beha
vior
el
emen
t (w
hose
exe
cutio
n st
art a
nd e
nd re
pres
ent t
he o
bser
ved
even
ts)
�S
uch
mod
elin
g co
nstru
cts
are
usef
ul fo
r com
plex
end
-to-e
nd fl
ows,
pro
vidi
ng fl
exib
le
mea
ns to
ann
otat
e an
alyz
er-d
efin
ed ti
min
g co
nstra
ints
.
/ MAR
TE T
utor
ial
165
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
166
Key
reas
onin
g fo
r the
use
of U
ML
in th
e sc
hedu
ling
anal
ysis
of R
TE S
yste
ms
�U
ML
is a
sta
ndar
d se
mi-v
isua
l lan
guag
e fo
r con
cept
ual m
odel
ing
that
ena
bles
the
usag
e of
a M
odel
Bas
ed a
ppro
ach
for s
oftw
are
and
syst
em e
ngin
eerin
g.
�M
DD
and
UM
L ha
ve b
een
broa
dly
intro
duce
d an
d us
ed in
prin
cipl
e b
y th
e so
ftwar
e en
gine
erin
g co
mm
unity
, and
hav
e re
ache
d a
sign
ifica
nt n
umbe
r of
prac
titio
ners
and
tool
sup
port.
�To
take
ben
efit
of th
is in
the
RTE
dom
ain,
they
nee
d to
be
capa
ble
of s
uppo
rting
th
e ne
cess
ary
(at l
east
tim
ing)
ver
ifica
tions
.
�Th
is le
ads
to th
e ne
cess
ity o
f mod
el b
ased
sch
edul
ing
anal
ysis
tech
niqu
es.
�A
s w
ell a
s th
e ne
cess
ity to
hav
e th
e m
odel
ing
elem
ents
to d
escr
ibe
the
plat
form
, th
e in
tera
ctin
g en
viro
nmen
t and
the
timin
g re
quire
men
ts.
/ MAR
TE T
utor
ial
167
Topi
cs
�A
flas
h of
sch
edul
abili
tyan
alys
is:
�R
MA
: the
foun
datio
ns�
Offs
et-b
ased
tech
niqu
es: h
igh
leve
l mod
els.
�S
truct
ural
and
beh
avio
ral m
odel
ing
elem
ents
in M
AR
TE.
�E
xam
ples
of t
he n
otat
ion
used
.
/ MAR
TE T
utor
ial
168
Opt
ions
for m
anag
ing
time
�C
ompi
le-ti
me
sche
dule
s:�
Kno
wn
as c
yclic
exe
cutiv
es�
Pre
dict
abili
ty th
roug
h st
atic
sch
edul
e�
Logi
cal i
nteg
rity
ofte
n co
mpr
omis
ed b
y tim
ing
stru
ctur
e�
Diff
icul
t to
mai
ntai
n, e
ven
with
mod
el b
ased
stra
tegi
es�
Run
-tim
e sc
hedu
les:
�P
riorit
y-ba
sed
sche
dule
rs�
Pre
empt
ive
or n
on p
reem
ptiv
e�
Fixe
d pr
iorit
y or
dyn
amic
prio
rity
�A
naly
tical
met
hods
nee
ded
for p
redi
ctab
ility
�S
epar
ates
logi
cal s
truct
ure
from
tim
ing
�S
erve
r-ba
sed
fram
ewor
ks:
�C
ombi
ne s
tatic
or d
ynam
ic s
ched
ules
with
bud
gets
and
per
iods
en
forc
ed a
t run
-tim
e
/ MAR
TE T
utor
ial
169
Fixe
d-P
riorit
y S
ched
ulin
g
�Fi
xed-
prio
rity
pree
mpt
ive
sche
dulin
g is
ver
y po
pula
r for
pr
actic
al a
pplic
atio
ns, b
ecau
se:
�Ti
min
g be
havi
or is
sim
pler
to u
nder
stan
d�
Trea
tmen
t of t
rans
ient
ove
rload
is e
asie
r to
pred
ict
�A
com
plet
e an
alyt
ical
tech
niqu
e ex
ists
�A
ll st
anda
rd c
oncu
rren
t lan
guag
es o
r ope
ratin
g sy
stem
s sp
ecify
fix
ed-p
riorit
y sc
hedu
ling
�FP
ana
lysi
s w
orks
als
o fo
r non
-pre
empt
ible
task
s or
non
-pr
eem
ptib
lese
ctio
ns
/ MAR
TE T
utor
ial
170
Rat
e M
onot
onic
Ana
lysi
s (R
MA
)
�R
ate
mon
oton
ic a
naly
sis
is a
n en
gine
erin
g ba
sis
for a
naly
zing
an
d de
sign
ing
real
-tim
e sy
stem
s.
�P
rovi
des
guid
elin
es fo
r opt
imum
prio
rity
assi
gnm
ent
�P
rovi
des
an a
naly
tical
fram
ewor
k fo
r ver
ifyin
g tim
ing
requ
irem
ents
�H
elps
to id
entif
y tim
ing
bottl
enec
ks a
nd e
rror
s
�Is
ver
y po
pula
r, an
d is
sup
porte
d by
con
curr
ent l
angu
ages
an
d re
al-ti
me
oper
atin
g sy
stem
s.
/ MAR
TE T
utor
ial
171
A s
impl
e ex
ampl
eTa
sks
Crit
ical
sec
tions
(Slid
e cr
edit:
M.G
onza
lez
Har
bour
& th
e S
EI R
MA
tuto
rial)
/ MAR
TE T
utor
ial
172
Why
Are
Dea
dlin
es M
isse
d?
�Fo
r a ta
sk to
mee
t its
dea
dlin
e, it
mus
t acc
omm
odat
e
�pr
eem
ptio
n: ti
me
wai
ting
for h
ighe
r-pr
iorit
y ta
sks
�ex
ecut
ion:
tim
e to
do
its o
wn
wor
k�
bloc
king
: tim
e de
laye
d by
low
er-p
riorit
y ta
sks
�Th
e ta
sk is
sch
edul
able
if th
e su
m o
f its
pre
empt
ion,
ex
ecut
ion,
and
blo
ckin
g is
less
than
its
dead
line
for t
he w
orst
po
ssib
le c
ase:
�E
xecu
tion
is u
navo
idab
le (u
nles
s re
quire
men
ts c
hang
e).
�P
reem
ptio
n ca
n be
min
imiz
ed b
y ch
oosi
ng a
n op
timum
prio
rity
assi
gnm
ent
�M
ain
focu
s: id
entif
y an
d re
duce
blo
ckin
g
/ MAR
TE T
utor
ial
173
Con
cept
s an
d D
efin
ition
s (p
erio
dic
task
s)
�P
erio
dic
task
�in
itiat
ed a
t fix
ed in
terv
als
�m
ust f
inis
h be
fore
sta
rt of
nex
t cyc
le
�Ta
sk’s
CP
U u
tiliz
atio
n: U
i= C
i/ T
i�
Ci=
com
pute
tim
e (e
xecu
tion
time)
for t
ask
ti�
T i=
perio
d of
task
t i�
Pi=
prio
rity
of ta
sk t i
�D
i= d
eadl
ine
of ta
sk t i
�f i
= ph
ase
of ta
sk t i
�R
i= re
spon
se ti
me
of ta
sk t i
�C
PU
util
izat
ion
for a
set
of t
asks
: U =
U1
+ U
2+
…+
Un
/ MAR
TE T
utor
ial
174
Bas
ic P
rinci
ples
of R
MA
�Tw
o co
ncep
ts h
elp
to b
uild
the
wor
st-c
ase
cond
ition
:
�C
ritic
al in
stan
t. Th
e w
orst
-cas
e re
spon
se ti
me
for a
ll ta
sks
in th
e ta
sk s
et is
obt
aine
d w
hen
all t
asks
are
act
ivat
ed a
t the
sam
e tim
e�
Che
ckin
g th
e fir
st d
eadl
ine.
Whe
n al
l tas
ks a
re a
ctiv
ated
at t
hesa
me
time,
if a
task
mee
ts it
s fir
st d
eadl
ine,
it w
ill a
lway
s m
eet a
ll of
its
dead
lines
�B
ased
on
thes
e co
ncep
ts, s
ever
al re
sults
aris
e:
�O
ptim
ality
of r
ate
mon
oton
ic p
riorit
ies
�U
tiliz
atio
n bo
und
test
�E
xact
test
/ MAR
TE T
utor
ial
175
Crit
ical
inst
ant
(Slid
e cr
edit:
M.G
onza
lez
Har
bour
& th
e S
EI R
MA
tuto
rial)
/ MAR
TE T
utor
ial
176
Util
izat
ion
and
Res
pons
e Ti
me
test
s
�U
tiliz
atio
n B
ound
(UB
) Tes
t: A
set
of n
inde
pend
ent p
erio
dic
task
s, w
ith d
eadl
ines
at t
he e
nd o
f the
per
iods
, sch
edul
ed b
y th
e ra
te m
onot
onic
alg
orith
m w
ill a
lway
s m
eet i
ts d
eadl
ines
, fo
r all
task
pha
sing
s, if
: U <
= U
(n) =
n(2
1/n
-1)
�C
ompl
etio
n tim
e te
st:
�Fo
r a n
umbe
r the
task
s ac
cord
ing
to p
riorit
y (h
ighe
st p
riorit
y=t 1,
lo
wes
t prio
rity=
t n)�
Und
er a
crit
ical
inst
ant c
ondi
tion,
the
amou
nt o
f wor
k W
i(t) o
f ta
sks
at p
riorit
y P
i or h
ighe
r sta
rted
befo
re t
is
/ MAR
TE T
utor
ial
177
Res
pons
e tim
e ev
alua
tion
�R
imay
be
com
pute
d by
the
follo
win
g ite
rativ
e fo
rmul
a:
�Th
e ite
ratio
n en
ds w
hen:
�Ta
sk t i
is s
ched
ulab
le if
:
/ MAR
TE T
utor
ial
178
Effe
cts
of ji
tter
�P
erio
dic
even
ts w
ith ji
tter h
ave
an a
rriv
al ti
me
whi
ch m
ay b
e ea
rly o
r lat
e, w
ithin
a b
ound
ed in
terv
al:
�ev
ents
arri
ve a
t
to
+ nT
+/-J
�Ji
tter m
ay h
ave
a de
lay
effe
ct o
n lo
wer
prio
rity
task
s
Exec
utio
n se
quen
ce fo
r tw
o pe
riodi
c ta
sks.
Ex
ecut
ion
sequ
ence
with
jitte
r.W
orst
cas
e is
one
pre
empt
ion
Wor
st c
ase
is tw
o pr
eem
ptio
ns
t
High
pri.
Low
pri.
t
(Slid
e cr
edit:
J.C
. Pal
enci
a &
M.G
onza
lez
Har
bour
)
/ MAR
TE T
utor
ial
179
Mor
e ge
nera
l app
roac
h
Rea
l-Tim
e Sy
stem
R
RR
�
Scen
ario
(inst
ance
) bas
ed, d
istr
ibut
ed, c
ontr
ol-fl
ow d
epen
denc
ies
(Slid
e cr
edit:
J.M
. Dra
ke)
/ MAR
TE T
utor
ial
180
Dis
tribu
ted
syst
em m
odel
Net
wor
kC
PU-2
CPU
-1
Line
ar A
ctio
n:
Line
ar R
espo
nse
to a
n Ev
ent:
a je j-
1,j
e j,j+
1T j
-1,j
= T
j = T
j,j+
1
a 1e 1
a 2e 1
,2a 3
e 2,3
d 2D
2ED
3
Action
e 1External
event
e 1Internal
event
(Slid
e cr
edit:
J.C
. Pal
enci
a &
M.G
onza
lez
Har
bour
)
/ MAR
TE T
utor
ial
181
Jitte
r in
dist
ribut
ed s
yste
ms
�Ji
tter i
n on
e pr
oces
sing
reso
urce
dep
ends
on
the
resp
onse
tim
es in
oth
er p
roce
ssin
g re
sour
ces
�R
espo
nse
times
dep
end
on ji
tters
CPU-
1
Netw
ork
CPU
-2
a 1a 5
a 3a 8
a 4a 7
a 6
a 2
a 1a 5
a 3a 8
a 4a 7
a 6
a 2
(Slid
e cr
edit:
J.C
. Pal
enci
a &
M.G
onza
lez
Har
bour
)
/ MAR
TE T
utor
ial
182
Tran
sact
ions
External
Event
(Action)
Precedence
Relationship
Step
Step
Fork
Timing
Requirement
Precedence
Relationship
(Multicast)
/ MAR
TE T
utor
ial
183
Tran
sact
ions
Step
Timing
Requirement
Usage
Shared
Resources
Schedulable
Resource
Processing
Resources
Scheduling
Parameters
Event
Event
Event
Reference
/ MAR
TE T
utor
ial
184
Res
ourc
es
/ MAR
TE T
utor
ial
185
«pr
ofile
»G
RM
«m
etac
lass
»U
ML:
:Cla
sses
::Ker
nel::
Cla
ssifi
er
resM
ult:
Inte
ger =
1is
Prot
ecte
d: B
oole
an
isAc
tive:
Boo
lean
«st
ereo
type
»R
esou
rce
«m
etac
lass
»U
ML:
:Cla
sses
::Ker
nel::
Inst
ance
Spec
ifica
tion
«st
ereo
type
»C
ompu
tingR
esou
rce
elem
entS
ize:
Inte
ger
«st
ereo
type
»C
omm
unic
atio
nMed
ia
sche
dPar
ams:
Sch
edPa
ram
eter
s [0
..*]
isAc
tive:
Bool
ean
= tru
e {Is
Rea
dOnl
y}
«st
ereo
type
»S
ched
ulab
leR
esou
rce
prot
ectK
ind:
Pro
tect
Prot
ocol
Kind
=prio
rityI
nher
itanc
ece
iling:
Inte
ger
othe
rPro
tect
Prot
ocol
: Stri
ngis
Prot
ecte
d:Bo
olea
n=tru
e{Is
Rea
dOnl
y}
«st
ereo
type
»M
utua
lExc
lusi
onR
esou
rce
«st
ereo
type
»D
evic
eRes
ourc
e
pack
etS
ize:
Inte
ger
«st
ereo
type
»C
omm
unic
atio
nEnd
Poin
t
elem
entS
ize:
Inte
ger
«st
ereo
type
»S
tora
geR
esou
rce
isPr
eem
ptib
le: B
oole
an =
true
sche
dPol
icy:
Sch
edPo
licyK
ind
= Fi
xedP
riorit
yot
herS
ched
Pol
icy:
Stri
ngsc
hedu
le: O
paqu
eExp
ress
ion
«st
ereo
type
»S
ched
uler
spee
dFac
tor:
NFP
_Rea
l = (v
alue
= 1
.0)
«st
ereo
type
»P
roce
ssin
gRes
ourc
e
«st
ereo
type
»Se
cond
aryS
ched
uler
«m
etac
lass
»U
ML:
:Cla
sses
::Ker
nel::
Prop
erty «st
ereo
type
»S
ynch
roni
zatio
nRes
ourc
e«
ster
eoty
pe»
Con
curr
ency
Res
ourc
e
«m
etac
lass
»U
ML:
:Inte
ract
ion:
:Bas
icIn
tera
ctio
ns::L
ifelin
e
«m
etac
lass
»U
ML:
:Com
posi
teSt
ruct
ures
::In
tern
alSt
ruct
ures
::C
onne
ctab
leE
lem
ent
«m
etac
lass
»U
ML:
:Com
posi
teSt
ruct
ures
::In
tern
alSt
ruct
ures
::C
onne
ctor
/ MAR
TE T
utor
ial
186
Dom
ain
mod
el fo
r Sch
edul
able
reso
urce
s
Sche
dulin
g
GR
M::R
esou
rceC
ore:
:R
esou
rce
GR
M::R
esou
rceM
anag
em
ent::
Res
ourc
eBro
ker
spee
dFac
tor:
NFP
_Rea
l =
(val
ue =
1.0
)
Proc
essi
ngR
esou
rce
brok
erbr
oked
Res
ourc
e
1..*
*
Sche
dula
bleR
esou
rce
GR
M::R
esou
rceM
anag
emen
t::A
cces
sCon
trol
Polic
y accC
trlP
olic
y*1.
.*
sche
dule
:Opa
queE
xpre
ssio
n
Sche
dule
r
GR
M::R
esou
rceT
ypes
::C
ompu
tingR
esou
rce
GR
M::R
esou
rceT
ypes
::C
omm
unic
atio
nMed
ia
1..*
*
proc
essi
ngU
nits
{Sub
set b
roke
dRes
ourc
e}1
0..*
sche
dula
bleR
esou
rce
host
GR
M::R
esou
rceT
ypes
::C
oncu
rren
cyR
esou
rce
Sche
dulin
gPar
amet
ers
sche
dPar
ams
*
1
0..1
mai
nSch
edul
er
Seco
ndar
ySch
edul
er
depe
nden
tSch
edul
er
virtu
alP
roce
ssin
gUni
ts1.
.*
0..1
1ho
st
polic
y{s
ubse
t acc
Ctrl
Pol
icy}
*1
polic
y: S
ched
Pol
icyK
ind
othe
rSch
edP
olic
y: S
tring
Sche
dulin
glPo
licy
Ear
liest
Dea
dlin
eFirs
tFI
FOFi
xedP
riorit
yLe
astL
axity
Firs
tR
ound
Rob
inTi
meT
able
Driv
enU
ndef
Oth
er«en
umer
atio
n»
Sche
dPol
icyK
ind
GR
M::R
esou
rceT
ypes
::D
evic
eRes
ourc
e
{isA
ctiv
e=Tr
ue}
/ MAR
TE T
utor
ial
187
Sha
red
reso
urce
s
/ MAR
TE T
utor
ial
188
Ext
ensi
ons
for s
ched
ulin
g«
prof
ile»
GR
M
sche
dpar
ams:
Sch
edPa
ram
eter
s[0.
.*]is
Activ
e:Bo
olea
n=tru
e{Is
Rea
dOnl
y}
«st
ereo
type
»S
ched
ulab
leR
esou
rce
prot
ectK
ind:
Pro
tect
Prot
ocol
Kind
=Prio
rityI
nher
itanc
ece
iling:
Inte
ger
othe
rPro
tect
Prot
ocol
: Stri
ngis
Prot
ecte
d:Bo
olea
n=tru
e{Is
Rea
dOnl
y}
«st
ereo
type
»M
utua
lExc
lusi
onR
esou
rce
isPr
eem
ptib
le: B
oole
an =
true
sche
dPol
icy:
Sch
edPo
licyK
ind
= Fi
xedP
riorit
yot
herS
ched
Polic
y: S
tring
sche
dule
: Opa
queE
xpre
ssio
n
«st
ereo
type
»Sc
hedu
ler
«st
ereo
type
»P
roce
ssin
gRes
ourc
e
«st
ereo
type
»S
econ
dary
Sch
edul
er
sche
dula
bled
Res
ourc
es
host
0..1
0..*
depe
nden
tSch
edul
ervi
rtual
Pro
cess
ingU
nits
0..*
0..1
host
0..1
proc
essi
ngU
nits
0..*
mai
nSch
edul
er0.
.1*
sche
dule
r0.
.1
prot
ecte
dSha
redR
esou
rces
«st
ereo
type
»C
ompu
tingR
esou
rce
/ MAR
TE T
utor
ial
189
Res
ourc
e U
sage
/ MAR
TE T
utor
ial
190
1.If
the
list u
sedR
esou
rces
is e
mpt
y th
e lis
t sub
Usa
ges
shou
ld n
ot b
e em
pty
and
vice
vers
a..
2.If
the
list u
sedR
esou
rces
has
only
one
ele
men
t, al
l the
opt
iona
l lis
ts o
f attr
ibut
es re
fer t
o th
is u
niqu
e R
esou
rce
and
at le
ast o
ne o
f the
m m
ust b
e pr
esen
t.3.
If th
e lis
t use
dRes
ourc
esha
s m
ore
than
one
ele
men
t, al
l of t
he o
ptio
nal l
ists
of
attri
bute
s th
at a
re
pres
ent,
mus
t hav
e th
at n
umbe
r of e
lem
ents
, and
they
will
be c
onsi
dere
d to
mat
ch o
ne to
one
.4.
If th
e lis
t sub
Usa
ges
is n
ot e
mpt
y, a
nd a
ny o
f th
e op
tiona
l lis
ts o
f at
tribu
tes
is p
rese
nt,
then
mor
e th
an o
ne a
nnot
atio
n fo
r the
sam
e re
sour
ce a
nd k
ind
of u
sage
may
be
expr
esse
d. In
this
cas
e, if
the
anno
tatio
ns h
ave
also
the
sam
e so
urce
and
sta
tistic
al q
ualif
iers
they
will
be c
onsi
dere
d in
con
flict
, an
d he
nce
the
Res
ourc
eUsa
gein
cons
iste
nt.
/ MAR
TE T
utor
ial
191
Stru
ctur
e of
SA
M
/ MAR
TE T
utor
ial
192
SA
M O
verv
iew
/ MAR
TE T
utor
ial
193
SA
M_W
orkl
oad:
End
-to-e
ndFl
ow
SA
M_W
orkl
oad
isS
ched
ulab
le: N
FP_B
oole
ansc
hedu
labi
lityS
lack
: NFP
_Rea
len
dToE
ndTi
me:
NFP
_Dur
atio
nen
dToE
ndD
eadl
ine:
NFP
_Dur
atio
n
EndT
oEnd
Flow
GQ
AM
_Wor
kloa
d::
Wor
kloa
dBeh
avio
r
wor
kloa
d1.
.*
patte
rn: A
rriv
alP
atte
rn
GQ
AM
_Wor
kloa
d::
Wor
kloa
dEve
ntG
QA
M_W
orkl
oad:
:B
ehav
iorS
cena
rioef
fect
1..*
inpu
tStre
am
1
1..*
endT
oEnd
Stim
uli
1en
dToE
ndR
espo
nseSA
M_O
bser
vers
::Ti
min
gObs
erve
r
Tim
ing
1
*
perio
dic:
Per
iodi
cPat
tern
aper
iodi
c: A
perio
dicP
atte
rnsp
orad
ic: S
pora
dicP
atte
rnbu
rst:
Bur
stP
atte
rnirr
egul
ar: I
rreg
ular
Pat
tern
clos
ed: C
lose
dPat
tern
open
: Ope
nPat
tern
«da
taTy
pe»
«ch
oice
Type
»A
rriv
alPa
ttern
/ MAR
TE T
utor
ial
194
SA
M_W
orkl
oad:
Beh
avio
rSce
nario
/ MAR
TE T
utor
ial
195
SA
M_O
bser
vers
SA
M_O
bser
vers
hard
soft
unde
fot
her
«en
umer
atio
n»
Laxi
tyK
ind
kind
: Con
stra
intK
ind
NFP
_Mod
elin
g::
NFP
_Ann
otat
ion:
:N
FP_C
onst
rain
t
laxi
ty: L
axity
Kin
d
GQ
AM
_Obs
erve
rs::
Tim
ingO
bser
ver
late
ncy:
NFP
_Dur
atio
nm
issR
atio
: NFP
_Rea
lut
ility
: Util
ityTy
pem
axJi
tter:
NFP
_Dur
atio
n
GQ
AM
_Obs
erve
rs::
Late
ncyO
bser
ver
Tim
eMod
els:
:Ti
med
Rel
ated
Entit
ies:
:Ti
med
Obs
erva
tions
::Ti
med
Inst
antO
bser
vatio
n
0..1
star
tObs
0..1
endO
bs
requ
ired
offe
red
cont
ract
«en
umer
atio
n»
Con
stra
intK
ind su
spen
sion
s: N
FP_I
nteg
erbl
ocki
ngTi
me:
NFP
_Dur
atio
nov
erla
ps: N
FP_I
nteg
er
Sche
dulin
gObs
erve
r
/ MAR
TE T
utor
ial
196
Res
ourc
es in
SA
M (d
omai
n m
odel
)
/ MAR
TE T
utor
ial
197
Res
ourc
es in
SA
M (p
rofil
e)
/ MAR
TE T
utor
ial
198
Exa
mpl
eC
lass
esVi
ew_T
eleo
pera
tedR
obot
read
(): D
ata
writ
e (D
: Dat
a)
data
: Int
eger
[*]
«pp
Uni
t»D
ispl
ayD
ata
upda
teD
ispl
ay ()
upda
teG
raph
ics
()
«rt
Uni
t»D
ispl
ayR
efre
sher
disp
layD
ata
proc
essE
vent
()pl
anTr
ajec
tory
()
«rt
Uni
t»C
omm
andI
nter
pret
er
disp
layD
ata
send
Com
man
d (C
: Com
man
d)aw
aitS
tatu
s ():
Sta
tus
Stat
ionC
omm
unic
atio
n
send
Sta
tus
(S: S
tatu
s)aw
aitC
omm
and
(): C
omm
and
Con
trol
lerC
omm
unic
atio
n
com
mco
mm
repo
rt ()
«rt
Uni
t»R
epor
ter
man
age
()«rt
Uni
t»C
omm
andM
anag
er
com
mco
mm
get (
): D
ata
set (
D: D
ata)
Dat
a: In
tege
r [*]
«pp
Uni
t»Se
rvos
Dat
a
cont
rolS
ervo
s ()
cont
rolA
lgor
ithm
s ()
doC
ontro
l ()
«rt
Uni
t»Se
rvos
Con
trol
ler
serv
osD
ata
serv
osD
ata
serv
osD
ata
Dep
loym
entV
iew
_Tel
eope
rate
dRob
ot
Stat
ion
Con
trol
ler
Rob
otA
rm
CA
N_B
us VM
E_B
us
/ MAR
TE T
utor
ial
199
Rea
l-tim
e si
tuat
ion
«ga
Wor
kloa
dBeh
avio
r» N
orm
alM
ode
«w
orkl
oadE
vent
»C
ontro
lTrig
g{ p
erio
dic
(per
iod=
(5, m
s)) }
«w
orkl
oadE
vent
»R
epor
tTrig
g{ p
erio
dic
(per
iod=
(100
, $pR
, ms)
) }
«w
orkl
oadE
vent
»R
epor
tTrig
g{ p
erio
dic
(per
iod=
(1, s
)) }
«gaS
cena
rio»
{ res
pTim
e= ($
r1, m
s),
utiliz
atio
n= $
u1,
exec
Tim
e= ($
e1, m
s) }
Con
trol
«gaS
cena
rio»
{ res
pTim
e= ($
r2, m
s),
utiliz
atio
n= $
u2,
exec
Tim
e= ($
wce
t1, m
ax, m
s) }
Rep
ort
«gaS
cena
rio»
{ res
pTim
e= ($
r3, m
s),
utiliz
atio
n= $
u3,
exec
Tim
e= ($
e3, m
s) }
Com
man
d
/ MAR
TE T
utor
ial
200
Pla
tform
«ga
Res
ourc
esPl
atfo
rm»
Tele
oper
ated
Rob
ot_P
latfo
rm
«sa
Exe
cHos
t»: C
ontro
ller
{ spe
edFa
ctor
= (1
.0)
cloc
kOvh
= (7
, us,
mea
s)cn
txtS
wT=
(5,
us, m
eas)
ISR
switc
hT=
(2.5
, us,
mea
s)sc
hedP
rioR
ange
= ([0
..30]
, det
erm
)IS
RP
rioR
ange
= ([3
1..3
1], d
eter
m) }
«sa
Com
mH
ost»
: CA
N_B
us{ t
rans
Mod
e= H
alf-D
uple
xsp
eedF
acto
r= ($
prC
AN
)bl
ockT
= (1
11, u
s, m
ax, m
eas)
pack
etT=
(64,
us,
cal
c) }
«sa
Exe
cHos
t»: S
tatio
n
«sa
Exe
cHos
t»: R
obot
Arm
«sc
hedu
labl
eRes
ourc
e»
: Com
man
dMan
ager
{ fp
(prio
rity=
16)
}
«sc
hedu
ler»
: RTO
S_S
ched
uler
{ sch
edP
olic
y= F
ixed
Prio
rity
}
«sc
hedu
labl
eRes
ourc
e»
: Ser
vosC
ontro
llerT
ask
{ fp
(prio
rity=
30)
}
«sc
hedu
labl
eRes
ourc
e»
: Rep
orte
r{ f
p (p
riorit
y= 2
4) }
«sc
hedu
labl
eRes
ourc
e»
: Con
trolle
rCom
m{ f
p (p
riorit
y= 3
1) }
«sc
hedu
labl
eRes
ourc
e»
: Msj
Sta
tus
{ fp
(prio
rity=
24)
}
«sc
hedu
labl
eRes
ourc
e»
: Msj
Com
man
d{ f
p (p
riorit
y= 2
4) }
«sc
hedu
labl
eRes
ourc
e»
: Dis
play
Ref
resh
erTa
sk{ f
p (p
riorit
y= 2
2) }
: VM
E_B
us
«allo
cate
»
«allo
cate
»
«allo
cate
»
«allo
cate
»
«allo
cate
»
«allo
cate
»
«allo
cate
»
/ MAR
TE T
utor
ial
201
A b
ehav
ior s
cena
rio
/ MAR
TE T
utor
ial
202
Par
amet
ric a
naly
sis
cont
exts
/ MAR
TE T
utor
ial
203
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s 17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
204
Per
form
ance
mod
elin
g fo
rmal
ism
s�
Diff
eren
t per
form
ance
mod
els
deve
lope
d ov
er ti
me
in th
e pe
rform
ance
ev
alua
tion
field
may
be
used
for p
erfo
rman
ce a
naly
sis
of U
ML
mod
els
�m
odel
tran
sfor
mat
ion
from
UM
L+M
AR
TE to
suc
h ta
rget
per
form
ance
m
odel
s ar
e ne
eded
�C
lass
ifica
tion
of p
erfo
rman
ce m
odel
s�
Ana
lytic
mod
els
�Q
ueue
ing
Net
wor
ks(Q
N)
�ca
ptur
e w
ell c
onte
ntio
n fo
r res
ourc
es�
effic
ient
ana
lytic
al s
olut
ions
exi
sts
for a
cla
ss o
f QN
(“se
para
ble”
QN
)�
Laye
red
Que
uein
gN
etw
orks
(QN
ext
ensi
on) a
re u
sed
as a
n ex
ampl
e�
Sto
chas
tic P
etri
Net
s�
good
flow
mod
els,
but
not
as
good
for r
esou
rce
cont
entio
n�
anal
ytic
al s
olut
ions
suf
fer f
rom
sta
te s
pace
exp
onen
tial e
xplo
sion
�S
toch
astic
Pro
cess
Alg
ebra
�in
trodu
ced
rece
ntly
by
mer
ging
Pro
cess
Alg
ebra
and
Mar
kov
Cha
ins
�S
imul
atio
n m
odel
s�
less
con
stra
ined
in th
eir m
odel
ing
pow
er, c
an c
aptu
re m
ore
deta
ils�
hard
er to
bui
ld a
nd m
ore
expe
nsiv
e to
sol
ve (r
un th
e m
odel
repe
ated
ly).
/ MAR
TE T
utor
ial
205
Que
uein
gN
etw
ork
(QN
): S
ingl
e S
ervi
ce C
ente
r
�P
aram
eter
s�
sche
dulin
g po
licy
(FIF
O, p
riorit
y, e
tc.)
�w
orkl
oad
inte
nsity
-ar
rival
pro
cess
�e.
g., P
oiss
on a
rriv
al w
ith ra
te 0
.5/s
econ
d�
serv
ice
dem
and
per c
usto
mer
�
e.g.
, exp
onen
tial d
istri
butio
n w
ith m
ean
of 1
.25
seco
nds
�P
erfo
rman
ce m
easu
res
�ut
iliza
tion
= pr
opor
tion
of ti
me
the
serv
er is
bus
y �
resi
denc
e tim
e =
aver
age
time
spen
t at t
he s
ervi
ce c
ente
r by
a cu
stom
er,
both
que
uein
gan
d re
ceiv
ing
serv
ice
�qu
eue
leng
th =
ave
rage
num
ber o
f cus
tom
ers
at th
e se
rvic
e ce
nter
�th
roug
hput
= ra
te a
t whi
ch c
usto
mer
s pa
ss th
roug
h th
e se
rvic
e ce
nter
.
arriv
ing
cust
omer
sde
part
ing
cust
omer
sQ
ueue
Serv
er
/ MAR
TE T
utor
ial
206
Sin
gle
Ser
vice
Cen
ter:
Per
form
ance
Res
ults
�Ty
pica
l non
-line
ar b
ehav
iour
for q
ueue
leng
th a
nd w
aitin
g tim
e�
serv
er re
ache
s sa
tura
tion
at a
cer
tain
arr
ival
rate
(util
izat
ion
clos
e to
1)
�at
low
wor
kloa
d in
tens
ity: a
n ar
rivin
g cu
stom
er m
eets
low
com
petit
ion,
so it
s re
side
nce
time
is ro
ughl
y eq
ual t
o its
ser
vice
dem
and
�as
the
wor
kloa
d in
tens
ity ri
ses,
con
gest
ion
incr
ease
s, a
nd th
e re
side
nce
time
alon
g w
ith it
�as
the
serv
ice
cent
er a
ppro
ache
s sa
tura
tion,
sm
all i
ncre
ases
in a
rriv
al ra
te
resu
lt in
dra
mat
ic in
crea
ses
in re
side
nce
time.
00.
10.
20.
30.
40.
50.
60.
70.
80.
91
00.
20.
40.
60.
8
Arriv
al R
ate
Utilization
05101520
00.
20.
40.
60.
8
Arriv
al ra
te
Residence Time
05101520
00.
20.
40.
60.
8
Arriv
al ra
te
Queue length
Util
izat
ion
Res
iden
ce T
ime
Que
ue le
ngth
/ MAR
TE T
utor
ial
207
Net
wor
k of
que
ues
�O
pen
and
clos
edQ
N m
odel
s
�A
dditi
onal
par
amet
ers
need
ed to
des
crib
e th
e m
ovem
ent o
f cus
tom
ers
thro
ugh
the
netw
ork
�e.
g., a
fter a
CP
U s
ervi
ce, a
cus
tom
ers
mov
es w
ith g
iven
pro
babi
litie
s or
vis
it ra
tios
to D
isk1
, Dis
k2 o
r out
�M
ultip
le c
usto
mer
cla
sses
�ea
ch c
lass
has
its
own
wor
kloa
d in
tens
ity, s
ervi
ce d
eman
ds a
nd v
isit
ratio
s�
com
bina
tion
of o
pen
and
clos
ed c
lass
es is
pos
sibl
e�
the
perfo
rman
ce re
sults
will
be
obta
ined
by
clas
s�
Bot
tlene
ck s
ervi
ce c
ente
r -m
ay b
e di
ffere
nt fo
r diff
eren
t cla
sses
.
CPU
Disk
1
Disk
2
out
CPU
CPU
Disk
D
isk
Disk
D
isk
out
CPU
Disk
1
Disk
2Te
rmin
als
. . .
CPU
Disk
Te
rmin
als
. . .
/ MAR
TE T
utor
ial
208
Laye
red
Que
uein
gN
etw
ork
(LQ
N) m
odel
ht
tp://
ww
w.s
ce.c
arle
ton.
ca/ra
ds/lq
n/lq
n-do
cum
enta
tion
�LQ
N is
a e
xten
sion
of Q
N�
mod
els
both
sof
twar
e ta
sks
(rect
angl
es)
and
hard
war
e de
vice
s(c
ircle
s)�
repr
esen
ts n
este
d se
rvic
es(a
ser
ver i
s al
so a
clie
nt to
oth
er s
erve
rs)
�so
ftwar
e co
mpo
nent
s ha
ve e
ntrie
sco
rresp
ondi
ng to
diff
eren
t ser
vice
s�
arcs
repr
esen
t ser
vice
requ
ests
(s
ynch
rono
us a
nd a
sync
hron
ous)
�m
ulti-
serv
ers
used
to m
odel
com
pone
nts
with
inte
rnal
con
curre
ncy
�W
hat w
e ge
t fro
m th
e LQ
N s
olve
r:�
Ser
vice
tim
e (m
ean,
var
ianc
e) in
clud
ing
nest
ed s
ervi
ces
�W
aitin
g tim
e�
Thro
ughp
ut�
Util
izat
ion
�P
roba
bilit
y of
mis
sing
a d
eadl
ine
(o
btai
ned
only
by
sim
ulat
ion)
clie
ntE
serv
ice1
serv
ice2
App
l
Que
ry1
Que
ry2
Clie
ntC
PU
Clie
ntT
DB
App
lC
PU DB
CP
UD
isk1
Dis
k2
task
entri
es
devi
ce
/ MAR
TE T
utor
ial
209
Per
form
ance
Ana
lysi
s in
MA
RTE
�In
MA
RTE
, “pe
rform
ance
mod
elin
g”de
scrib
es th
e an
alys
is o
f tem
pora
l pr
oper
ties
of b
est-e
ffort
syst
ems
and
soft-
real
-tim
e em
bedd
ed s
yste
ms
�e.
g., i
nfor
mat
ion
proc
essi
ng s
yste
ms,
web
-bas
ed a
pplic
atio
ns a
nd s
ervi
ces,
en
terp
rise
syst
ems,
mul
timed
ia, t
elec
omm
unic
atio
ns.
�P
erfo
rman
ce m
easu
res
(ana
lysi
s ou
tput
s) a
re s
tatis
tical
, suc
h as
:�
mea
n th
roug
hput
and
del
ay (r
espo
nse
time)
�m
ean
queu
e le
ngth
and
que
uein
gde
lay
�pr
obab
ility
of m
issi
ng a
targ
et re
spon
se ti
me
�re
sour
ce u
tiliz
atio
n�
Inpu
t par
amet
ers
to th
e an
alys
is m
ay a
lso
be p
roba
bilis
tic, s
uch
as:
�ra
ndom
arri
val p
roce
ss,
�ra
ndom
exe
cutio
n tim
e fo
r an
oper
atio
n�
prob
abilit
y of
a c
ache
hit.
�
Com
mon
per
form
ance
ana
lysi
s te
chni
ques
incl
ude:
�
sim
ulat
ions
�qu
euei
ngne
twor
ks a
nd e
xten
ded
queu
eing
netw
orks
�di
scre
te-s
tate
mod
els
such
as
Sto
chas
tic P
etri
Net
s or
Sto
chas
tic P
roce
ssA
lgeb
ra.
�be
havi
or is
ofte
n re
gard
ed a
s no
n-te
rmin
atin
g fo
r the
pur
pose
s of
ana
lysi
s
(ste
ady
stat
e be
havi
or ra
ther
than
tran
sien
t beh
avio
r).
/ MAR
TE T
utor
ial
210
Per
form
ance
Ana
lysi
s M
odel
(PA
M) o
verv
iew
�P
AM
dom
ain
mod
el e
mpl
oys
and
exte
nds
the
GQ
AM
dom
ain
mod
el.
�W
orkl
oad:
�P
AM
em
ploy
s fe
atur
es s
uch
as th
e W
orkl
oadE
vent
desc
riptio
n of
a s
tream
of
arriv
ing
even
ts�
focu
s on
som
e w
orkl
oad
type
s: o
pen
and
clos
ed a
rriva
ls, w
orkl
oad
gene
rato
rs
and
trace
s�
Beh
avio
r Sce
nario
:�
PA
M u
ses
the
beha
vior
-cau
salit
y m
odel
of S
cena
rios
and
Ste
ps�
exte
nds
the
prop
ertie
s of
a S
tep
to in
clud
e m
ore
kind
s of
ope
ratio
n de
man
ds
durin
g a
step
�ad
ds th
e po
ssib
ility
of a
n as
ynch
rono
us (n
on-s
ynch
roni
zing
) par
alle
l ope
ratio
n,
whi
ch is
fork
ed b
ut n
ever
join
s �
othe
r Ste
p ex
tens
ion:
a P
assR
esou
rce
step
whi
ch in
dica
tes
the
pass
ing
of a
sh
ared
reso
urce
from
one
pro
cess
to a
noth
er�
Res
ourc
es:
�Im
porta
nt re
sour
ces
incl
ude
hard
war
e E
xecu
tionE
ngin
es, c
oncu
rrent
pro
cess
th
read
s (S
ched
uled
Res
ourc
es),
and
Logi
calR
esou
rces
defin
ed b
y th
e so
ftwar
e�
logi
cal r
esou
rce
exam
ples
: sem
apho
re, l
ock,
buf
fer p
ool,
criti
cal s
ectio
n�
intro
duce
s a
spec
ial c
once
pt o
f Run
Tim
eObj
ectIn
stan
ceas
an
alia
s fo
r a p
roce
ss
or th
read
poo
l res
ourc
e id
entif
ied
in b
ehav
ior s
peci
ficat
ions
by
othe
r ent
ities
(suc
h as
life
lines
and
sw
imla
nes)
�
Obs
erve
rs: u
ses
the
GQ
AM
obs
erve
r con
cept
s.
/ MAR
TE T
utor
ial
211
Ana
lysi
sCon
text
para
met
ers
�A
naly
sisC
onte
xt(fr
om G
QA
M) c
orre
spon
ds to
the
scop
e of
an
eval
uatio
n.
�co
mbi
nes
the
syst
em re
pres
ente
d by
its
beha
viou
rand
reso
urce
s w
ith o
ne o
r m
ore
wor
kloa
ds.
�gi
ves
a se
t of m
odel
par
amet
ers
for d
efin
ing
the
rang
e of
var
iatio
n of
cas
es th
at
will
be a
naly
sed
�O
ne U
ML
spec
ifica
tion
may
giv
e ris
e to
sev
eral
per
form
ance
mod
els,
due
to
varia
tions
in s
yste
m u
sage
, wor
kloa
d, a
lloca
tion,
dep
loym
ent,
and
conf
igur
atio
n�di
ffere
nt m
odel
s ca
ses.
�P
aram
eter
ized
NFP
sar
e su
ppor
ted
by th
e us
e of
:�
varia
bles
glo
bal t
o th
e A
naly
sisC
onte
xt�
varia
ble
nam
es in
pla
ce o
f num
eric
val
ues
of in
put p
rope
rties
for m
odel
ele
men
ts.
�fu
nctio
nal d
epen
denc
ies
of th
e in
put p
rope
rties
on
the
glob
al v
aria
bles
, to
defin
e va
lues
.U
ML
Mod
elC
onfig
urat
ion/
Plat
form
Cas
e Pa
ram
eter
sPe
rfor
man
ceM
odel
Inpu
t NFP
s(P
aram
eter
Valu
es)
Perf
orm
ance
Mod
el S
olve
r
Out
put N
FPs
(Per
form
ance
Res
ults
)
/ MAR
TE T
utor
ial
212
Info
rmal
vie
w o
f Ser
vice
s an
d B
ehav
ior
�S
ervi
ce is
an
impo
rtant
con
cept
in p
erfo
rman
ce�
requ
ests
for s
ervi
ce m
ay h
ave
a re
quire
d qu
ality
of s
ervi
ce�
an a
ctua
l ser
vice
is d
efin
ed b
y a
Beh
avio
rSce
nario
, with
a p
rovi
ded
qual
ity o
f ser
vice
�S
ervi
ces
may
be
inco
rpor
ated
into
the
mod
eled
beh
avio
r in
thre
e w
ays:
�by
mak
ing
a se
rvic
eDem
and
from
a S
tep
to a
Req
uest
edS
ervi
ce, r
epre
sent
ing
an
oper
atio
n of
fere
d at
som
e in
terfa
ce, w
hich
is in
turn
def
ined
by
a B
ehav
iorS
cena
rio�
by m
akin
g a
beha
vior
Dem
and
from
a S
tep
to d
irect
ly in
voke
a B
ehav
iorS
cena
rio�
by m
akin
g an
ext
OpD
eman
dfro
m a
Ste
p to
requ
est a
n ex
tern
al s
ervi
ce, w
hich
is
defin
ed in
the
perfo
rman
ce e
nviro
nmen
t out
side
the
UM
L m
odel
.
«us
e»
PWor
kloa
dGen
erat
or
GQ
AM
_Wor
kloa
d ::
Beh
avio
rSce
nario
Beh
avio
rSce
nario
PReq
uest
edSe
rvic
eEx
tern
alO
pera
tion
«us
e»
«us
e»
«us
e»
Beh
avio
rSce
nario
PReq
uest
edSe
rvic
eEx
tern
alO
pera
tion
«us
e»
«us
e»
«us
e»
«us
e»
«us
e»
«us
e»
GQ
AM
_Wor
kloa
d ::
GQ
AM
_Wor
kloa
d ::
/ MAR
TE T
utor
ial
213
PA
M D
omai
n M
odel
: Wor
kloa
d an
d B
ehav
ior
Wor
kloa
d
Beh
avio
rSc
enar
io
Step
s
/ MAR
TE T
utor
ial
214
PA
M S
tep
exte
nsio
ns�
Ext
erna
l res
ourc
e de
man
ds�
Res
ourc
es w
hich
are
not
mod
eled
with
in th
e so
ftwar
e de
sign
may
hav
e an
impa
ct
on p
erfo
rman
ce (e
.g.,
disk
acc
ess)
. �
PA
M d
omai
n m
odel
iden
tifie
s "e
xter
nal o
pera
tions
" by
such
reso
urce
s by
a n
ame
(a s
tring
), so
they
can
be
mod
eled
in th
e pe
rform
ance
mod
el.
�D
eman
ds b
y a
Ste
p fo
r ext
erna
l ope
ratio
ns a
re d
escr
ibed
by
the
pair
of p
rope
rties
ex
tern
alO
pDem
and
and
exte
rnal
OpC
ount
: �
exte
rnal
OpD
eman
dis
an
orde
red
list o
f ope
ratio
n na
mes
(stri
ngs)
�ex
tern
alO
pCou
ntis
an
orde
red
list (
in th
e sa
me
orde
r) of
the
num
ber o
f dem
ands
mad
e du
ring
one
exec
utio
n of
the
Ste
p (m
ay b
e an
inte
ger,
an a
vera
ge v
alue
or a
pro
babi
lity
dist
ribut
ion)
�D
irect
dem
ands
to a
Beh
avio
rSce
nario
�D
eman
ds b
y a
Ste
p di
rect
ly to
a B
ehav
iorS
cena
rioar
e de
scrib
ed b
y th
e pa
ir of
pr
oper
ties
beha
vDem
and
and
beha
vCou
nt�
“noS
ync”
attri
bute
�
folo
win
ga
fork
due
to a
n as
ynch
rono
us m
essa
ge o
r par
ope
rand
, “no
Syn
c”ex
plic
itly
indi
cate
that
the
resp
ectiv
e pa
ralle
l bra
nch
does
not
join
�
beha
viou
rusi
ng n
oSyn
cm
ay p
rovi
de in
crea
sed
conc
urre
ncy
and
incr
ease
d pe
rform
ance
.
/ MAR
TE T
utor
ial
215
PA
M D
omai
n M
odel
: Res
ourc
es�
Logi
calR
esou
rce:
def
ined
by
the
softw
are
-has
to b
e m
odel
ed a
s a
reso
urce
in th
e pe
rform
ance
mod
el b
ecau
se it
intro
duce
s de
lays
�R
unTi
meO
bjec
tInst
ance
: alia
s fo
r a p
roce
ss o
r thr
ead
pool
reso
urce
id
entif
ied
in b
ehav
ior s
peci
ficat
ions
by
lifel
ines
and
sw
imla
nes
/ MAR
TE T
utor
ial
216
Com
mun
icat
ion
Cha
nnel
�A
mes
sage
bet
wee
n tw
o ob
ject
s is
con
veye
d by
som
e m
echa
nism
:�
if th
e ob
ject
s ar
e in
the
sam
e ex
ecut
ion
thre
ad (p
roce
ss),
it is
conv
eyed
by
the
lang
uage
runt
ime
�if
the
obje
cts
are
in d
iffer
ent e
xecu
tion
thre
ads
(pro
cess
es) i
nth
e sa
me
node
(Pro
cess
ingH
ost)
it is
con
veye
d by
the
oper
atin
g sy
stem
�if
the
obje
cts
are
on d
iffer
ent n
odes
, it i
s co
nvey
ed b
y a
syst
em
laye
r nam
ed h
ere
Com
mC
hann
el, w
hich
may
be:
�a
mid
dlw
are
laye
r (C
OR
BA
con
nect
ion,
Jav
a R
emot
e M
etho
d In
voca
tion,
web
ser
vice
s co
nnec
tion,
etc
.) �
a M
essa
ge-P
assi
ng In
terfa
ce c
onne
ctio
n in
a g
rid
�a
sock
et o
r sec
ure
sock
et c
onne
ctio
n �
a m
ore
com
plex
infra
stru
ctur
e su
ch a
s a
publ
ish-
and-
subs
crib
e sy
stem
.
/ MAR
TE T
utor
ial
217
Mod
elin
g C
omm
unic
atio
n C
hann
els
1.W
ithin
the
sam
e no
de, l
angu
age
runt
ime
and
oper
atin
g sy
stem
cos
ts a
re
cons
ider
ed p
art o
f the
sce
nario
. �
the
inte
r-pr
oces
s co
mm
unic
atio
n co
st p
er b
yte
of th
e ho
st m
ay b
e us
ed to
ca
lcul
ate
the
host
Dem
and
for c
omm
unic
atio
n.2.
Bet
wee
n no
des
the
defa
ult i
s:
�de
term
ine
node
hos
tDem
ands
from
the
send
ing
and
rece
ivin
g ov
erhe
ad o
n th
e no
des
�in
sert
the
late
ncy
of th
e lin
k (a
n at
tribu
te o
f the
con
nect
ing
Com
mun
icat
ions
Hos
t)3.
Bet
wee
n no
des
the
conv
eyan
ce o
f the
mes
sage
may
als
o be
mod
eled
as
an e
xter
nal o
pera
tion,
invo
king
a s
ubm
odel
of th
e co
mm
unic
atio
ns la
yer
�th
is m
ay b
e an
attr
activ
e op
tion
for m
odel
ing
the
beha
viou
rof t
he In
tern
et
4.B
etw
een
node
s a
com
mun
icat
ions
laye
r suc
h as
CO
RB
A m
ay b
e de
fined
as
a U
ML
Stru
ctur
edC
lass
offe
ring
send
and
rece
ive
oper
atio
ns to
the
two
end-
poin
t pro
cess
es
�su
ch a
laye
r is
deno
ted
as a
Com
mC
hann
elw
ith a
con
veya
nce
oper
atio
n de
man
ded
by th
e C
omm
unic
atio
nsS
tep
in th
e sc
enar
io
�its
ser
vice
is d
efin
ed b
y a
Beh
avio
rSce
nario
, whi
ch m
ay in
volv
e di
rect
ory
look
-up
s, a
utho
rizat
ion
and
redi
rect
ion
of re
ques
ts
5.B
etw
een
node
s a
com
plex
com
mun
icat
ions
pro
toco
l can
be
mod
eled
by
a pu
re B
ehav
iorS
cena
riono
t ass
ocia
ted
with
a s
yste
m c
ompo
nent
, but
de
scrib
ing
a co
llabo
ratio
n of
the
host
s.
/ MAR
TE T
utor
ial
218
Com
mun
icat
ion
chan
nels
at d
iffer
ent d
etai
l lev
els
Sch
edul
able
Res
ourc
e(s
ende
r)
Com
mun
icat
ions
Hos
t (li
nk)
-late
ncy
hard
war
e-ba
sed
conv
eyan
ce
Sch
edul
able
Res
ourc
e(r
ecei
ver)
M
essa
ge
Pro
cess
ingH
ost
(rec
eive
r)
-r
ecvO
H
Pro
cess
ingH
ost
(sen
der)
-sen
dOH
Sch
edul
able
Res
ourc
e(s
ende
r)
stru
ctur
ed c
onve
yanc
e
Sch
edul
able
Res
ourc
e(re
ceiv
er)
Mes
sage
syst
em-w
ide
beha
vior
B
ehav
iorS
cena
rio
(sen
der t
o re
ceiv
er)
com
mun
icat
ions
laye
r C
omm
Cha
nnel
(s
ende
r to
rece
iver
)
{one
or t
he o
ther
}
Det
ail L
evel
2, c
onve
yanc
e m
odel
ed a
t the
har
dwar
e le
vel
Det
ail l
evel
s 4
and
5: u
sing
a c
omm
unic
atio
ns la
yer
/ MAR
TE T
utor
ial
219
PA
M D
omai
n M
odel
s: C
omm
unic
atio
n�
The
dom
ain
mod
el to
sup
port
com
mun
icat
ions
mod
elin
g co
ntai
ns:
�C
omm
Cha
nnel
: mec
hani
sm fo
r con
veyi
ng m
essa
ges
�C
omm
unic
atio
nSte
p: s
peci
aliz
atio
n of
Ste
p ha
ndlin
g m
essa
ge
com
mun
icat
ion
/ MAR
TE T
utor
ial
220
PA
M P
rofil
e: W
orkl
oad,
Beh
avio
r, O
bser
ver
Wor
kloa
d
Beh
avio
rSc
enar
ioSt
eps
Ana
lysi
sC
onte
xt
Obs
erve
r
/ MAR
TE T
utor
ial
221
PA
M P
rofil
e: R
esou
rces
/ MAR
TE T
utor
ial
222
Exa
mpl
e 1:
TC
P-W
dep
loym
ent
«exe
cHos
t»db
Hos
t:{c
omm
Rcv
Ove
rhea
d=
(0.1
4,m
s/K
B),
com
mTx
Ove
rhea
d=
(0.0
7,m
s/K
B),
max
RI=
3}
«exe
cHos
t»eb
Hos
t:{c
omm
Rcv
Ove
rhea
d=
(0.1
5,m
s/K
B),
com
mTx
Ove
rhea
d=
(0.1
,ms/
KB
),m
axR
I= 5
}
«exe
cHos
t»w
ebSe
rver
Hos
t:{c
omm
Rcv
Ove
rhea
d=
(0.1
,ms/
KB
),co
mm
TxO
verh
ead
= (0
.2,m
s/K
B)}
«com
mH
ost»
inte
rnet
:{b
lock
ingT
ime
= (1
00,u
s)}
«dep
loy»
«dep
loy»
: Dat
abas
e: W
ebSe
rver
«arti
fact
»w
ebSe
rver
A
: EB
row
ser
«arti
fact
»eb
A«a
rtifa
ct»
data
base
A
«dep
loy»
«man
ifest
»«m
anife
st»
«man
ifest
»
«com
mH
ost»
lan:
{blo
ckin
gTim
e=
(10,
us),
capa
city
= (1
00,M
b/s)
}
/ MAR
TE T
utor
ial
223
Exa
mpl
e 1:
sim
ple
scen
ario
«PaR
unTI
nsta
nce»
web
Serv
er: W
ebSe
rver
{poo
lSiz
e=
(web
thre
ads=
80),
inst
ance
= w
ebse
rver
}
«PaR
unTI
nsta
nce»
data
base
: Dat
abas
e{p
oolS
ize
= (d
bthr
eads
=5),
inst
ance
= d
atab
ase}
eb: E
Bro
wse
r
«paS
tep»
« paC
omm
Step
»
2:
{hos
tDem
and
= (1
2.4,
ms)
,re
p =
(1.3
,-,m
ean)
,m
sgS
ize
= (2
,KB
)}
«PaC
omm
Step
»
4:
{msg
Siz
e=
(75,
KB
)}
«paC
omm
Step
»3:
{msg
Siz
e=
(50,
KB
)}
«PaS
tep»
«PaW
orkl
oadE
vent
»
1:
{ope
n (in
terA
rrT=
(exp
(17,
ms)
)),
{hos
tDem
and
= (4
.5,m
s)}
/ MAR
TE T
utor
ial
224
Exa
mpl
e 1:
Get
Hom
ePag
esc
enar
io
par
imag
eser
ver
<<Pa
Run
TIns
tanc
e>>
{inst
ance
= im
ages
erve
r}
web
serv
er<<
PaR
unTI
nsta
nce>
>{in
stan
ce =
web
serv
er}
data
base
<<Pa
Run
TIns
tanc
e>>
{inst
ance
= d
atab
ase}
brow
ser
<<Pa
Run
TIns
tanc
e>>
{inst
ance
= b
row
ser}
Prom
otio
n1<<
PaSt
ep{p
rob
= 0.
4}
<<G
aPer
form
ance
Con
text
>> {c
onte
xtPa
ram
s=N
user
s, T
hink
Tim
e, Im
ages
, R}
8:
2: g
etC
usto
mer
Dat
a<<
PaSt
ep>>
{hos
tDem
and
= (2
,ms)
}
4: g
etLi
stO
fSub
ject
s<<
PaSt
ep>>
{hos
tDem
and=
(10,
ms)
}
6: lo
gInt
erac
tion
<<Pa
Step
>> {n
oSyn
c}
7: g
etH
omeI
mag
es<<
PaSt
ep>>
{hos
tDem
and
= (0
.5,m
s),
rep
= Im
ages
,ex
tOpD
eman
d=
"Imag
eFile
Op"
,ex
tOpC
ount
= 1}
9: <
<PaC
omm
Ste
p>>
{msg
Size
= (3
.4 +
5*Im
ages
)}
3: 5:
<<G
aWor
kloa
dEv
ent>
> {c
lose
d (p
opul
atio
n=N
user
s),
extD
elay
=Thi
nkTi
me)
}
<<Pa
Com
mSt
ep>>
{msg
Size
=(2.
9, K
B)}
1: g
etH
omeP
age
[firs
t pro
mo]
[els
e]
alt
<<Pa
Step
>> {r
ep=0
.2}
[if c
usto
mer
is lo
gged
in]
opt
ref
<<Pa
Step
>> {h
ostD
eman
d=
(1,m
s),
resp
T={(
(1,s
,per
cent
95),r
eq),
((R
,s,p
erce
nt95
),cal
c)}
Prom
otio
n2<<
PaSt
ep{p
rob
= 0.
6}re
f
/ MAR
TE T
utor
ial
225
Exa
mpl
e 1:
Get
Hom
ePag
esc
enar
io (f
ragm
ent)
web
serv
er<<
PaR
unTI
nsta
nce>
>{in
stan
ce =
web
serv
er}
data
base
<<P
aRun
TIns
tanc
e>>
{inst
ance
= d
atab
ase}
brow
ser
<<P
aRun
TIns
tanc
e>>
{inst
ance
= b
row
ser}
<<G
aPer
form
ance
Con
text
>> {c
onte
xtP
aram
s=N
user
s, T
hink
Tim
e, Im
ages
, R}
2: g
etC
usto
mer
Dat
a
<<P
aSte
p>>
{hos
tDem
and
= (2
,ms)
}
3:
<<G
aWor
kloa
dE
vent
>> {c
lose
d (p
opul
atio
n=N
user
s),
extD
elay
=Thi
nkTi
me)
}
<<P
aCom
mS
tep>
> {m
sgS
ize=
(2.9
, KB
)}
1: g
etH
omeP
age
<<P
aSte
p>>
{rep=
0.2}
[if c
usto
mer
is lo
gged
in]
opt
<<P
aSte
p>>
{hos
tDem
and
= (1
,ms)
,re
spT=
{((1,
s,pe
rcen
t95)
,req)
,((R
,s,p
erce
nt95
),cal
c)}
/ MAR
TE T
utor
ial
226
Wor
kloa
d ge
nera
tor f
or T
PC
-W
<<P
aSte
p>>
Get
Prod
uctD
etai
ls{b
lock
T=
(10,
s),
beha
vDem
and
= ge
tPro
duct
Det
ails
,be
havC
ount
= 1}
<<Pa
Ste
p>>
Get
Hom
ePag
e{b
ehav
Dem
and
= ge
tHom
ePag
e,be
havC
ount
= 1,
bloc
kT=
(4,s
)}
<<P
aSte
p>>
Shop
ping
Car
t{b
lock
T=
(12,
s),
beha
vDem
and
= Sh
oppin
gCar
t,be
havC
ount
= 1}
<<P
aSte
p>>
New
Prod
ucts
{beh
avD
eman
d=
new
Prod
uct,
beha
vCou
nt=
1, b
lock
T=
(4,s
)}
<<P
aSte
p>>
Che
ckou
t{b
lock
T=
(45,
s),
beha
vCou
nt=
1,be
havD
eman
d=
Che
ckou
t}
<<G
aAna
lysi
sCon
text
>> {c
onte
xtP
aram
s=(N
user
s)}
<<G
aWor
kloa
dGen
erat
or>>
{pop
ulat
ion=
Nus
ers}
<<P
aSte
p>>
{pro
b=
0.7}
<<P
aSte
p>>
{pro
babi
lity
= 0.
9}
<<P
aSte
p>>
{pro
babi
lity
= 0.
3}
<<P
aSte
p>>
{pro
babi
lity
= 0.
1}
/ MAR
TE T
utor
ial
227
Exa
mpl
e 2:
Bui
ldin
g S
urve
illan
ce S
yste
m
�Tw
o fre
quen
tly e
xecu
ted
scen
ario
s ar
e ch
osen
for
perfo
rman
ce a
naly
sis
Acc
ess
cont
rol
Log
entry
/ ex
it
Acq
uire
/sto
re
vide
o
Man
age
acce
ss ri
ghts
Man
ager
Dat
abas
eV
ideo
Cam
era
<<in
clud
es>>
Use
r
/ MAR
TE T
utor
ial
228
Bui
ldin
g S
urve
illanc
e S
yste
m: d
eplo
ymen
t
<<G
aAna
lysi
sCon
text
>>{c
onte
xtP
aram
s=
{Nbu
ffers
}, {p
aram
Val
ues=
15}}
<<G
aExe
cHos
t>>
Con
trol
Nod
e<<
GaE
xecH
ost>
>
Cam
era
<<G
aExe
cHos
t>>
Dat
aBas
eNod
e
<<R
esou
rce>
>bu
fferp
ool
{max
RI=
Nbu
ffers
}
: Acq
uire
: Cam
eraC
ontr
ol: S
tore
<<ar
tifac
t>>
Buf
Mgr
: DB
<<ar
tifac
t>>
DB
<<ar
tifac
t>>
Stor
e<<
artif
act>
>C
ontr
ol<<
Sche
dula
bleR
esou
rce>
>
<<ar
tifac
t>>
Acq
uire
<<de
ploy
>>
<<de
ploy
>>
<<G
aCom
mH
ost>
>
Bac
kend
{cap
acity
= (1
000,
Mb/
s)}
<<de
ploy
>>
<<G
aCom
mH
ost>
>
LAN
{cap
acity
= (1
00,M
b/s)
<<de
ploy
>><<
depl
oy>>
<<de
ploy
>>
<<m
anife
st>>
<<m
anife
st>>
<<m
anife
st>>
<<m
anife
st>>
/ MAR
TE T
utor
ial
229
Bui
ldin
g S
urve
illan
ce S
yste
m: A
cqui
reV
ideo
scen
ario
<<P
aSte
p>>
getO
neIm
age
{rep
etiti
ons
= N
cam
eras
}
{hos
tDem
and
= (1
.8,m
s)}
<<P
aS
tep>
>ge
tBuf
getIm
age
pass
Imag
e{h
ostD
eman
d=
(0.2
,ms)
}
<<P
aS
tep>
>fr
eeB
uf
{hos
tDem
and
= (2
.5,m
s), n
oSyn
c}
<<P
aS
tep>
>st
oreI
mag
e
clea
nUp
<<P
aSte
p>>
<<G
aAcq
uire
Ste
p>>
allo
cBuf
fer
{hos
tDem
and
= (0
.5,m
s),
acqR
es=
buffe
rpoo
l,re
sUni
ts=
1}
<<P
aS
tep>
><<
GaR
elea
seS
tep>
>de
allo
cBuf
fer
{hos
tDem
and
= (0
.5,m
s),
relR
es=
buffe
rpoo
l}
<<P
aS
tep>
>st
oreD
B{h
ostD
eman
d=
{((bl
ocks
*0.9
),m
s,m
ean)
,(b
lock
s*0.
2),v
ar)},
extO
pDem
and
= w
riteB
lock
,ex
tOpC
ount
= bl
ocks
}
bufM
gr{m
axR
I= 1
,in
stan
ce=
Acq
uire
}
DB
{max
RI=
DB
Thre
ads,
inst
ance
= D
B}
Stor
e{m
axR
I= s
tore
Thre
ads
,in
stan
ce=
Sto
re}
<<P
aRun
TIns
tanc
e>>
Acq
uire
{ins
tanc
e=
Acq
uire
,m
axR
I= a
cqui
reTh
read
s}
cycl
eIni
t
<<G
aAna
lysi
sCon
text
>>{c
onte
xPar
ams=
{Nca
mer
as, f
ram
eSiz
e, b
lock
s, a
cqui
reTh
read
s,
stor
eThr
eads
, DB
Thre
ads}
,pa
ram
Val
ues
= {1
00, 0
.1, 1
5, 1
, 2, 2
}}
<<P
aCom
mS
tep>
>{m
sgS
ize
= (fr
ameS
ize,
byte
s),
repe
titio
ns =
(fra
meS
ize/
1500
)}<<
PaR
esP
assS
tep>
>{p
assR
es=
buffe
rpoo
l,re
sUni
ts=
1}
<<G
aWor
kloa
dEve
nt>>
{clo
sed
(pop
ulat
ion=
1),
inte
rOcc
Tim
e=
{(1.0
,s,p
erce
nt95
,req)
,(C
ycle
Tim
e95,
s,pe
rcen
t95,
calc
)}<<
PaS
tep>
> {ho
stD
eman
d=
(0.2
, ms)
,ex
tDel
ay=
(0, s
)}
<<P
aRun
TIns
tanc
e>>
<<P
aRun
TIns
tanc
e>>
<<P
aRun
TIns
tanc
e>>
/ MAR
TE T
utor
ial
230
From
UM
L+M
AR
TE to
per
form
ance
mod
els
�W
hat i
s ne
eded
for p
erfo
rman
ce a
naly
sis
in a
UM
L m
odel
ex
tend
ed w
ith M
AR
TE a
nnot
atio
ns:
�ke
y us
e ca
ses
desc
ribed
by
repr
esen
tativ
e sc
enar
ios
�fre
quen
tly e
xecu
ted,
with
per
form
ance
con
stra
ints
�re
sour
ces
used
by
each
sce
nario
�re
sour
ce ty
pes:
act
ive
or p
assi
ve, p
hysi
cal o
r log
ical
, har
dwar
eor
so
ftwar
e�
quan
titat
ive
reso
urce
dem
ands
for e
ach
scen
ario
ste
p �
how
muc
h, h
ow m
any
times
?
�w
orkl
oad
inte
nsity
for e
ach
scen
ario
�op
en w
orkl
oad:
arr
ival
rate
of r
eque
sts
for t
he s
cena
rio�
clos
ed w
orkl
oad:
num
ber o
f sim
ulta
neou
s us
ers
/ MAR
TE T
utor
ial
231
Dire
ct U
ML
to L
QN
Tra
nsfo
rmat
ion:
ou
r firs
t app
roac
h�
Gen
erat
e LQ
N m
odel
stru
ctur
e(ta
sks,
dev
ices
and
thei
r int
er-
conn
ectio
ns) f
rom
:�
UM
L m
odel
of t
he h
igh-
leve
l sof
twar
e ar
chite
ctur
e�
UM
L de
ploy
men
t dia
gram
�G
ener
ate
LQN
det
aile
d el
emen
ts(e
ntrie
s, p
hase
s, a
ctiv
ities
an
d th
eir p
aram
eter
s) fr
om:
�U
ML
mod
els
of k
ey s
cena
rios
with
per
form
ance
ann
otat
ions
�S
cena
rios
can
be re
pres
ente
d in
UM
L by
the
softw
are
desi
gner
s as
:�
inte
ract
ion
diag
ram
s�
activ
ity d
iagr
ams
/ MAR
TE T
utor
ial
232
UM
L �
LQN
Tra
nsfo
rmat
ion
Alg
orith
m
/ MAR
TE T
utor
ial
233
allo
c[0
.5, 0
]
getIm
age
[12,
0]pa
ssIm
age
[0.9
, 0]
proc
One
Imag
e[1
.5,0
]A
cqui
rePr
oc
Buf
ferM
anag
er
bufE
ntry
Buf
fer
Acq
uire
Proc
2lo
ck[0
, 500
]Lo
ck
rele
aseB
uf[0
.5, 0
]B
ufM
gr2
alar
m[0
,0]
Ala
rm
netw
ork
[0, 1
]N
etw
ork
(infin
ite)
stor
eIm
age
[3.3
, 0]
Stor
ePro
cUse
rra
te=0
.5/s
ecU
sers
read
Car
d[1
, 0]
Car
dRea
der
adm
it[3
.9,0
.2]
Acc
essC
ontro
ller
writ
eEve
nt[1
.8, 0
]w
riteI
mg
[7.2
,0]
read
Rig
hts
[1.8
,0] writ
eRec
[3, 0
]w
riteB
lock
[1, 0
]re
adD
ata
[1.5
,0]
(1,0
)
(for
war
ded)
(1, 0
)(1
, 0)
(0, 1
)
($P,
0)
(1, 0
)(1
, 0)
($N
)
(1,0
)
(0, 0
.2)
($B
, 0)
(0.4
, 0)
(1, 0
)(for
war
ded)
(0,0
)
App
licC
PU
DB
CPU Dis
kPLock
P
Ala
rmP
Car
dP
Use
rP
Net
P
Dum
my
Dat
aBas
e(1
0 th
read
s)
Dis
k(2
thre
ads)
(1)
(1,0
)
acqu
ireLo
op[1
.8]
Vid
eoC
ontro
ller
bufE
ntry
Buf
fer
proc
One
Imag
e[1
.5,0
]A
cqui
rePr
oc
acqu
ireLo
op[1
, 8]
Vid
eoC
ontro
ller
stor
eIm
age
Stor
ePro
c[3
.3, 0
]
App
licC
PU
LQN
mod
el o
f a B
uild
ing
Sur
veill
ance
Sys
tem
/ MAR
TE T
utor
ial
234
proc
One
Imag
e[1
.5,0
]
allo
c[0
.5, 0
]
bufE
ntry
getIm
age
[12,
0]pa
ssIm
age
[0.9
, 0]A
cqui
rePr
oc
Buf
ferM
anag
er
Buf
fer
Acq
uire
Proc
2
acqu
ireLo
op[1
.8]
Vid
eoC
ontro
ller
lock
[0, 5
00]
Lock
rele
aseB
uf[0
.5, 0
]B
ufM
gr2
alar
m[0
,0]
Ala
rm
netw
ork
[0, 1
]N
etw
ork
(infin
ite)
stor
eIm
age
[3.3
, 0]
Stor
ePro
cUse
rra
te=0
.5/s
ecU
sers
read
Car
d[1
, 0]
Car
dRea
der
adm
it[3
.9,0
.2]
Acc
essC
ontro
ller
writ
eEve
nt[1
.8, 0
]w
riteI
mg
[7.2
,0]
read
Rig
hts
[1.8
,0] writ
eRec
[3, 0
]w
riteB
lock
[1, 0
]re
adD
ata
[1.5
,0]
(1,0
)
(for
war
ded)
(1, 0
)(1
, 0)
(0, 1
)
($P,
0)
(1, 0
)(1
, 0)
($N
)
(1,0
)
(0, 0
.2)
($B
, 0)
(0.4
, 0)
(1, 0
)(for
war
ded)
(0,0
)
App
licC
PU
DB
CPU Dis
kPLock
P
Ala
rmP
Car
dP
Use
rP
Net
P
Dum
my
Dat
aBas
e(1
0 th
read
s)
Dis
k(2
thre
ads)
(1)
(1,0
)
proc
One
Imag
e[1
.5,0
]A
cqui
rePr
oc
acqu
ireLo
op[1
.8]
Vid
eoC
ontro
ller
(1,0)
LQN
mod
el o
f a B
uild
ing
Sur
veill
ance
Sys
tem
/ MAR
TE T
utor
ial
235
Usi
ng th
e LQ
N M
odel
for I
mpr
ovem
ents
�B
ase
case
sys
tem
cap
acity
: 20
cam
eras
�pr
oble
m:s
oftw
are
bottl
enec
kat
the
buffe
rs�
Add
ing
sof
twar
e re
sour
ces:
�4
Buf
fers
and
2 S
tore
Pro
cth
read
s �
resu
lt: 4
0 ca
mer
as s
uppo
rted,
per
form
ance
impr
ovem
ent 1
00%
�ne
xt p
robl
em:h
ardw
are
bottl
enec
kat
the
proc
esso
r�
Rep
licat
ing
the
proc
esso
r:�
Dua
l App
licat
ion
CP
U�
resu
lt: 5
0 ca
mer
as s
uppo
rted,
per
form
ance
impr
ovem
ent 1
50%
�In
crea
sing
sof
twar
e co
ncur
renc
y le
vel
�us
e as
ynch
rono
us m
essa
ges
–ra
ise
conc
urre
ncy
leve
l�
resu
lt: 1
00 c
amer
as s
uppo
rted,
per
form
ance
impr
ovem
ent 4
00%
/ MAR
TE T
utor
ial
236
Age
nda
09:0
0 am
to 1
0:15
am
�In
trodu
ctio
n to
MD
D fo
r RT/
E s
yste
ms
�M
AR
TE fo
unda
tions
10:1
5 am
to 1
0:45
am
: Bre
ak
10:4
5 am
to 1
2:00
am
�H
igh-
leve
l mod
elin
g co
nstru
cts
�D
etai
led
softw
are
and
hard
war
e pl
atfo
rms
mod
elin
g 13
:00
pm to
14:
30 p
m: L
unch
14
:30
pm to
15:
00 p
m�
Intro
duct
ion
to m
odel
-bas
ed R
TE a
naly
sis
15:0
0 pm
to 1
5:45
pm
�M
odel
-bas
ed s
ched
ulab
ility
anal
ysis
15
:45
pm to
16:
15 p
m: B
reak
16
:15
pm to
17:
00 p
m�
Mod
el-b
ased
per
form
ance
ana
lysi
s17
:00
pm to
17:
30 p
m�
Con
clus
ions
and
per
spec
tives
abo
ut M
AR
TE
/ MAR
TE T
utor
ial
237
MA
RTE
Fro
ntie
rs a
nd C
halle
nges
�M
AR
TE d
efin
e th
e la
ngua
ge c
onst
ruct
s on
ly!
�C
omm
on p
atte
rns,
bas
e bu
ildin
g bl
ocks
, sta
ndar
d N
FP a
nnot
atio
ns�
Gen
eric
con
stra
ints
that
do
not f
orce
spe
cific
exe
cutio
n m
odel
s,an
alys
is
tech
niqu
es o
r im
plem
enta
tion
tech
nolo
gies
�It
does
not
cov
er m
etho
dolo
gies
asp
ects
:�
Inte
rface
-Bas
ed D
esig
n, D
esig
n S
pace
Exp
lora
tion
�M
eans
to m
anag
e re
finem
ent o
f NFP
mea
sure
men
t mod
els
�C
oncr
ete
proc
esse
s to
sto
rage
, bin
d, a
nd d
ispl
ay N
FP c
onte
xt m
odel
s�
Map
ping
to tr
ansf
orm
MoC
Cs
into
ana
lysi
s m
odel
s
MA
RTE
is to
the
RTE
S do
mai
n as
UM
L to
the
Syst
em &
Softw
are
dom
ain:
a fa
mily
of l
arge
and
ope
nsp
ecifi
catio
n fo
rmal
ism
s!
/ MAR
TE T
utor
ial
238
Que
stio
ns ?
�w
ww
.mar
tes.
org
�W
orks
hop
on m
odel
-bas
edde
sign
and
val
idat
ion
for R
TES
�H
eld
in c
o,nj
unct
ion
with
trhe
Mod
els
2007
con
fere
nce
�w
ww
.pro
mar
te.o
rg�
The
MA
RTE
web
site
�w
ww
.pap
yrus
uml.o
rg�
On
open
sou
rce
Ecl
ipse
plu
g-in
for U
ML2
gra
phic
alm
odel
ing
�M
AR
TE im
plem
enta
tion
avai
labl
een
d of
Jul
y w
ithin
the
V1.
7 re
lale
ase
of th
e to
ol�
Alre
ady
avai
labl
eon
:�
http
s://s
peed
y.su
pele
c.fr/
Pap
yrus
/svn
/Pap
yrus
/ext
ensi
ons/
MA
RTE
/hea
d/�
Wor
king
on:
�ht
tps:
//spe
edy.
supe
lec.
fr/P
apyr
us/s
vn/P
apyr
us/c
ore/
...
![UML1 EER vs. UML Terminology EER Diagram Entity Type Entity Attribute Domain Composite Attribute ~ [Derived Attribute] Relationship Type Relationship Instance.](https://static.fdocuments.us/doc/165x107/56649dba5503460f94aaa962/uml1-eer-vs-uml-terminology-eer-diagram-entity-type-entity-attribute-domain.jpg)
