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Transcript of Combining State and Model-based approaches for Mobile Agent Load Balancing Georgousopoulos Christos...
Combining State and Model-based approaches for Mobile Agent Load Balancing
Georgousopoulos Christos
Omer F. Rana
http://www.cs.cf.ac.uk/Digital-Library/
Load balancing overviewLoad balancing overview
Load balance
mobile static
state model
Market mechanism
Specialized agentsgather System state
information
Aim: improve the average utilization and performance of tasks on available servers
Kinds of Load Balance (LB):
Keren & Barak:mobile LB has a
30-40% improvement over
the static placement scheme
• only a price• sophistiated auction protocols• a pricing mechanism without any negotiation
• roam through the network• bid for resources
Our approach on LBOur approach on LB
Provide a LB mechanism to evenly distribute agent tasks among the available servers
(i.e. equitably server the agents, there are no priorities between agents based on the time needed for their task to be accomplished)
We propose a LB mechanism based on a combination of the model-based and state-based approaches
(i.e. decisions on LB are based upon a model which adapts due to the information gathered from the state-based approach)
We demonstrate this approachfor a MAS operating on an active digital library composed of multi-spectral images of the Earth as part of the Synthetic Aperture Radar Atlas (SARA)
The SARA LB mechanismThe SARA LB mechanism
State-based approach
Model-based approach
(4/4) Communication between management agents
(1/4) The management agents in the SARA architecture
(3/4) Information maintained by management agents
(2/4) Distribution of information among the management agents
(1/1) LB decision model
The SARA architectureThe SARA architecture
E X S A
U R A S
U R A
U R A
A G E N T E NV IR O N M E N T
A G E N T E NV IR O N M E N T
LA A LR A
LM AU A A
U M A
LS A
LIG A
D B
FILEA R C H IV E
C O M P U TES E RV E R
M E TA -DATA
U R A
LA A LR A
LM A
LS A
LIG A
W eb S erver
Voyage r p la tform
Voyage r p la tform
FIPA -O S p la tfo rm
FIPA -O S p la tfo rm
E X S A
U R A
A G E N T E NV IR O N M E N T
U A A
U M A
W eb S erver
Voyage r p la tform
FIPA -O S p la tfo rm
CLIENT
EX M AS
EX M AS
CLIENT
EX M AS
W eb SERVER 1
In form ation SERVER 1 In form ation SERVER 2
U R A S
A G E N T E NV IR O N M E N T
Voyage r p la tform
FIPA -O S p la tfo rm
EX M AS
W eb SERVER 2
m essag e exchang e
creation of a gent
M anagem ent agent’s in teraction
m ovem ent
sen d/rece ive req uest
h idden arch itec tura l deta ils
F IPA -com pliant g atew ay
U IA : User In terface A gent
U R A : U ser R equ est A gentU A A : U ser A sss tant A gent
LIA : Loca l In terface A g entLA A :LM A :U M A :
LS A : L IG A: U R A S : E X S A :
Loca l A ss istant A gent Local M ana gem ent A gent
U niversa l M anage m en t A g ent
Local S ecurity A gentLocal In terG ratio n A gent
U R A’s S ervantE xterm al S e rvice A gent
LR A : Local R etrieva l A gen t
D B
FILEA R C H IV E
C O M P U TES E RV E R
M E TA -DATA
E X S A
U R A S
U R A
U R A
A G E N T E NV IR O N M E N T
A G E N T E NV IR O N M E N T
LA A LR A
LM AU A A
U M A
LS A
LIG A
D B
FILEA R C H IV E
C O M P U TES E RV E R
M E TA -DATA
U R A
LA A LR A
LM A
LS A
LIG A
W eb S erver
Voyage r p la tform
Voyage r p la tform
FIPA -O S p la tfo rm
FIPA -O S p la tfo rm
E X S A
U R A
A G E N T E NV IR O N M E N T
U A A
U M A
W eb S erver
Voyage r p la tform
FIPA -O S p la tfo rm
CLIENT
EX M AS
EX M AS
CLIENT
EX M AS
W eb SERVER 1
In form ation SERVER 1 In form ation SERVER 2
U R A S
A G E N T E NV IR O N M E N T
Voyage r p la tform
FIPA -O S p la tfo rm
EX M AS
W eb SERVER 2
m essag e exchang e
creation of a gent
M anagem ent agent’s in teraction
m ovem ent
sen d/rece ive req uest
h idden arch itec tura l deta ils
F IPA -com pliant g atew ay
U IA : User In terface A gent
U R A : U ser R equ est A gentU A A : U ser A sss tant A gent
LIA : Loca l In terface A g entLA A :LM A :U M A :
LS A : L IG A: U R A S : E X S A :
Loca l A ss istant A gent Local M ana gem ent A gent
U niversa l M anage m en t A g ent
Local S ecurity A gentLocal In terG ratio n A gent
U R A’s S ervantE xterm al S e rvice A gent
LR A : Local R etrieva l A gen t
D B
FILEA R C H IV E
C O M P U TES E RV E R
M E TA -DATA
The SARA architectureThe SARA architecture
EXSA
U R AS
U R A
U R A
AG EN T ENVIR O N M EN T
AG EN T ENVIR O N M EN T
LAA LR A
LM AU AA
U M A
LSA
LIG A
D B
FILEAR C H IVE
C O M PU TESERVER
M ETA-DATA
U R A
LAA LR A
LM A
LSA
LIG A
W eb Server
Voyage r p la tform
Voyage r p la tform
FIPA-O S platfo rm
FIPA-O S platfo rm
EXSA
U R A
AG EN T ENVIR O N M EN T
U AA
U M A
W eb Server
Voyage r p la tform
FIPA-O S platfo rm
CLIENT
EX MAS
EX MAS
CLIENT
EX MAS
Web SERVER 1
Inform ation SERVER 1 Inform ation SERVER 2
U R AS
AG EN T ENVIR O N M EN T
Voyage r p la tform
FIPA-O S platfo rm
EX MAS
Web SERVER 2
m essag e exchang e
creation of a gent
M anagem ent agent’s in teraction
m ovem ent
sen d/receive req uest
h idden architec tura l deta ils
F IPA-com pliant g atew ay
U IA : User In terface Agent
U R A: U ser R equ est AgentU AA: U ser Asss tant Agent
LIA : Loca l In terface Ag entLAA :LM A:U M A:
LSA : LIG A: U R AS: EXSA:
Local Ass istant Agent Local M ana gem ent Agent
U niversal M anage m en t Ag ent
Local Security AgentLocal In terG ratio n Agent
U R A’s ServantExterm al Se rvice Agent
LR A: Local R etrieval Agen t
D B
FILEAR C H IVE
C O M PU TESERVER
M ETA-DATA
(1/4) The management agents in the SARA architecture (1/4) The management agents in the SARA architecture
Info. server LMA (Local Management Agent)
web server UMA (Universal Management Agent)
i) optimize mobile agents’ itinerary
ii) avoid unnecessary migrations
iii) identification & comparison of agent task
i) inform mobile agents for updates
A management agent exists for every server
Their common objective: optimize system performance
Why multiple management agents ?
i) no central point of failure
ii) over a centralized scheme: as the number of agents increase, the network load is increased
(state-based approach)(state-based approach)
LB decisions are supported through the management agents
Minimization of information transmitted over the network
Minimization of the mobile agent’s size
System optimization
Advantages of having management agents control over LB decisionsAdvantages of having management agents control over LB decisions
(i.e. only 2 messages are exchanged between a mobile agent and a management agent: the agent’s requirements & the agent’s itinerary )
(i.e. the decision support algorithm is within the management agents. Alternatively mobile agents would have to carry it during their migration)
Information used for LB decisions may also be reused for:
i) undertaking similarity analysis between agent requests i.e. tasksii) cache techniques are possible to be applied
iii) lay the foundations for an efficient monitoring system
(2/4) Distribution of information among the management agents(2/4) Distribution of information among the management agents
distributed scheme :information is distributed among the servers
centralized scheme :a global database is used to hold all information for each server
ii) map of the surrounding area
i) global network map
iii) neighbor map
- agent interactions
- information:
- in a case of a failure
stored in one locationnetwork overload increases
- impose agents to have a kind of intelligence
- each server has all the information: replication (for integrity)
no central point of failure
network overload decreases(provides all information for each server)
(provides information for the local server but information is reduced more and more for servers which are not in the local region)
(provides information for the local server and its neighbor servers only)
(state-based approach)(state-based approach)
(3/4) Information maintained by management agents(3/4) Information maintained by management agents(state-based approach)(state-based approach)
LMA’s information
LMA’s information acquired by
Local: resources: software: status of voyager server, available analysis algorithms hardware: database server: status, processing power compute server: status, processing power, average data filtered per sec., maximum data filtered per sec.
local LAA
number of agent: active, persistent general (concerning database server): average completion task time, average server’s utilisation
LMA itself
Remote: servers’ resources:…
LMAs
servers’ bandwidths: server x with server y sender agent
UMA’s information
UMA’s information acquired by
Local agent’s info: agent id: general: request, time of request
local UAA(upon URA’s creation)
time of request accomplished, status of the task location of results: server’s IP, physical location path, file-space acquired resources used: software: analysis algorithm (AA) used, size of custom AA hardware: database/file archives used, engagement time (from-to), server’s utilization (before-after), compute server used, engagement time (from-to)
local UAA(before URA’s death)
Remote agents’ info: server x,y: agent id, request, status of the task
UMAs
LMAs’ info: server x, y: … LMAs
SARA LB uses the global network map for decentralized information distribution with a slight variation …
(4/4) Communication between management agents(4/4) Communication between management agents(state-based approach)(state-based approach)
Management agents’ interaction
Event Interaction(sender – recipient)
Information exchange Type of mes.
on the initialization of the system LMA-LMAs/UMAs contents in row 1,2 of table 1 multicast
upon URA’s creation UAA-local UMA contents in row 1 of table 2 direct
UMA-UMAs information in bold of table 2 multicast
before URA’s death UAA-local UMA contents in row 2 of table 2 direct
UMA-UMAs information in bold, in row 2 of table 2 multicast
URA’s migration failure URA-local LMA/UMA
Voyager server is down (row 1, table 1) direct
LMA/UMA-LMAs/UMAs
multicast
database connection failure LRA-local LMA database is unavailable (row 1, table 1) direct
LMA-LMAs/UMAs multicast
sever will be unavailable until a specified time
LMA-LMAs/UMAs the time the server will become available (row 1, table 1)
multicast
need for further information about an agent’s task
UMA-UMA selected information of row 1,2 of table 2 based on the recipient UMA needs
direct
change on information-server’s (LMA’s) status/resources
LAA-local LMA contents in row 1 of table 1 direct
LMA-LMAs/UMAs contents in row 1,2 of table 1 multicast
change on UMA’s information (concerning URA personal details)
UMA-UMAs contents in row 3 of table 2 multicast
LB decision modelLB decision model(model-based approach)(model-based approach)
i) agents’ tasksii) servers’ utilization (performance load)
iii) availability of resourcesiv) network efficiency
LB decisions are based on a model which accepts as:
input: an agent’s requirements & System state informationoutput: the appropriate server where an agent should migrate to
The model is a function of:
A g e n t’s Task
N e ed f ilte r in g
N e ed f ilte r in gP a rtia lly th e sa m eE x a c tly th e sam e
D o no t ne ed f ilte r in g
D o no t ne ed f ilte r in g
C u s to m fil te r
C u s to m fil te r
S e rv e r fi lte r
S e rv e r fi lte r.. ... .
S im ila r (ca ch ed ) N o t s im ila r (no t cach e d )
LB decision modelLB decision model(model-based approach)(model-based approach)
The model may be better expressed with reference to the agents’ task…
LB decision modelLB decision model(model-based approach)(model-based approach)
W ill b ec o m eav a ilab le a t T
c s
F o r u n k n o w n tim e
F o r u n k n o w n tim e
C o m p u te se rv e ris u n av a ilab le
C o m p u te se rv e ris a v a ilab le
S e rv e r is a v a ilab le
(iii)
(iv)
(v )
(v i)
(v ii)
(v iii)
(ix)
(ii)
(i)
W ill b ec o m eav a ilab le a t T
s
N o
Ye s
W ill b ec o m eav a ilab le a t T
c s
F o r u n k n o w n tim e
C o m p u te se rv e ris u n av a ilab le
C o m p u te se rv e ris a v a ilab le
W ill b ec o m eav a ilab le a t T
c s
F o r u n k n o w n tim e
C o m p u te se rv e ris u n av a ilab le
C o m p u te se rv e ris a v a ilab le
BS
UUT codea
av
sav
x2
.*
case 3:Agent’s task Similar (cashed) Exactlythe same Need filtering Custom filter
case 5:Agent’s task Not similar (not chased) Do not need filtering
where:Tav = the average time an agent needs to complete a task (regarding all servers) Uav = the average utilization of all serversUs = the utilization of a serverSa.code = the file-size of an agent’s code.B2 = the bandwidth between 2 information serversΤs = time needed for a server to became available
LU
*
utilization of a server
where:a = the number of agents on that serverμ = the average task time of the agentsL = the processing power of the server
examples of different agents’ tasks…
+Ts
Parameters server 1 server 2 server 3 server 4 server 5 server 6 server 7 server 8 server 9 server 10
Agents (α) = 14 22 18 9 13 26 7 25 25 12
Av.Task.T (μ) = 17 27 25 22 27 16 31 12 11 13
Proc.Power (L) = 13 22 22 9 10 27 13 26 25 8
Utiliz.Server (Us) = 18.3 27 20.45 22 35.1 15.4 16.6 11.5 11 19.5
AgentCode (Sa.code)= 7148 7148 7148 7148 7148 7148 7148 7148 7148 7148
Bandwidth Sx-Sy (B2)= 2412 2104 2355 2005 2500 1704 2006 2205 1988 2055
Av.Util.allServ (Uav) = 19.6 19.6 19.6 19.6 19.6 19.6 19.6 19.6 19.6 19.6
Av.Task (Tav) = 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1
x = 21.73 31.09 24.01 26.13 38.85 19.99 20.59 15.04 14.88 23.48
0
5
10
15
20
25
30
35
40
45
server 1 server 2 server 3 server 4 server 5 server 6 server 7 server 8 server 9 server 10
Agent (α) Av.Task.T (μ) Proc.Power (L) x
LB decision modelLB decision model(model-based approach)(model-based approach)
Agent’s task Not similar (not chased) Do not need filtering
Mathematica simulation of Case 5
( L= x*100000 ops )
( Sa = Kbytes/sec )
( B2 = Kbytes/sec )
Advantages of the proposed LB techniqueAdvantages of the proposed LB technique
LB decisions are supported by the management agents
Distribution of information between the management agents
More accurate LB decisions
(the variation of the global network map decentralized information distribution implies reduction of information replication)
(LB model uses the state-based information)
Conclusion – Future workConclusion – Future work
were specialized stationary agents are used to gather system state information and make decisions on the distribution of mobile agents among the servers,
based on a model of probabilistic estimations in relation with the information provided by the stationary agents
we demonstrated a combination of the state and model-based approaches for mobile agent load balancing
implement the proposed LB technique…
… to optimize the intelligence of the management agents