Post on 06-Feb-2020
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 1/15
Analysis of Markov Reward Models withPartial Reward Loss Based on a Time
Reverse Approach
Gábor Horváth, Miklós TelekTechnical University of Budapest, 1521 Budapest, Hungary
{hgabor,telek}@webspn.hit.bme.hu
Outline
● Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 2/15
Outline
■ Markov Reward models with reward loss
■ The difficulty of time forward approach
■ The time reverse analysis approach
■ Properties of the obtained solution
■ Numerical examples
■ Conclusions
Outline
Introduction
● Markov Reward models
without reward loss● Markov Reward models with
total reward loss● Markov Reward models with
partial reward loss
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 3/15
Markov Reward models without reward loss
Markov reward models (MRM)
■ a finite state CTMC,■ non negative reward rates (ri),■ performance measures:
◆ reward accumulated up to time t,◆ time to accumulate reward w.
ri
rk
rj
rk
t
t
j
i
k
Z(t)
B(t)
Outline
Introduction
● Markov Reward models
without reward loss● Markov Reward models with
total reward loss● Markov Reward models with
partial reward loss
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 4/15
Markov Reward models with total reward loss
We consider
■ first order MRM (deterministic dependence on Z(t)),■ without impulse reward,■ but with potential reward loss at state transition.
t
rk
t
Z(t)
B(t)
j
i
k
ri
rkrj
Outline
Introduction
● Markov Reward models
without reward loss● Markov Reward models with
total reward loss● Markov Reward models with
partial reward loss
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 5/15
Markov Reward models with partial reward loss
In case of partial reward loss:
■ αi remaining portion of reward when leaving state i,■ the lost reward is proportional to:
◆ total accumulated reward⇒ partial total loss,
◆ reward accumulated in the last state⇒ partial incremental loss.
Outline
Introduction
● Markov Reward models
without reward loss● Markov Reward models with
total reward loss● Markov Reward models with
partial reward loss
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 5/15
Markov Reward models with partial reward loss
In case of partial reward loss:
■ αi remaining portion of reward when leaving state i,■ the lost reward is proportional to:
◆ total accumulated reward⇒ partial total loss,
◆ reward accumulated in the last state⇒ partial incremental loss.
t
ri
rk
rj
rk
tT1 T2 T3
Z(t)
B(t)
i
j
k
B(T−1 )αi
B(T−2 )αk
B(T−3 )αjαj[B(T−3 )− B(T2)]
t
ri
rk
rj
B(T2)
rk
rkαk
riαi
rjαj
tT1 T2 T3
Z(t)
B(t)
i
j
k
Outline
Introduction
Model behaviour
● Time forward approach
● Time reverse approach
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 6/15
Time forward approach
Possible interpretation:■ Reduced (riαi) reward accumulation up to the last state
transition,■ and total (ri) reward accumulation in the last statewithout reward loss.
αj[B(T−3 )− B(T2)]
t
ri
rk
rj
B(T2)
rk
rkαk
riαi
rjαj
tT1 T2 T3
Z(t)
B(t)
i
j
k
Outline
Introduction
Model behaviour
● Time forward approach
● Time reverse approach
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 6/15
Time forward approach
Possible interpretation:■ Reduced (riαi) reward accumulation up to the last state
transition,■ and total (ri) reward accumulation in the last statewithout reward loss.
αj[B(T−3 )− B(T2)]
t
ri
rk
rj
B(T2)
rk
rkαk
riαi
rjαj
tT1 T2 T3
Z(t)
B(t)
i
j
k
Unfortunately, the last state transition before time T is not astopping time.
Outline
Introduction
Model behaviour
● Time forward approach
● Time reverse approach
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 7/15
Time reverse approach
Behaviour of the time reverse process:■ Inhomogeneous CTMC
with initial probability←−γ (0) = γ(T )
and generator←−Q(τ) = {←−qij(τ)},
where
←−qij(τ) =
γj(T − τ)
γi(T − τ)qji if i 6= j,
−∑
k∈S,k 6=i
γk(T − τ)
γi(T − τ)qki if i = j.
Outline
Introduction
Model behaviour
● Time forward approach
● Time reverse approach
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 7/15
Time reverse approach
Behaviour of the time reverse process:■ Inhomogeneous CTMC
with initial probability←−γ (0) = γ(T )
and generator←−Q(τ) = {←−qij(τ)},
where
←−qij(τ) =
γj(T − τ)
γi(T − τ)qji if i 6= j,
−∑
k∈S,k 6=i
γk(T − τ)
γi(T − τ)qki if i = j.
■ Total (ri) reward accumulation in the first state,■ and reduced (riαi) reward accumulation in all consecutive
states■ without reward loss.
Outline
Introduction
Model behaviour
Model description
● Time reverse approach
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 8/15
Time reverse approach
Potential model description:
duplicate the state space to describe■ the total reward accumulation in the first state (ri),■ and the reduced reward accumulation in all further states
(riαi).
Outline
Introduction
Model behaviour
Model description
● Time reverse approach
Analysis approach
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 8/15
Time reverse approach
Potential model description:
duplicate the state space to describe■ the total reward accumulation in the first state (ri),■ and the reduced reward accumulation in all further states
(riαi).
π∗(0) = [γ(T ), 0],←−Q∗(τ) =
←−−QD(τ)
←−Q(τ)−
←−−QD(τ)
0←−Q(τ)
, R∗ =
R 0
0 Rα
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 9/15
Inhomogeneous differential equation
Introducing
←−Y i(τ, w) = Pr(
←−B (τ) ≤ w,
←−Z (τ) = i)
we can apply the analysis approach available forinhomogeneous MRMs.
It is based on the solution of the inhomogeneous partialdifferential equation
∂
∂τ
←−Y (τ, w) +
∂
∂w
←−Y (τ, w)R =
←−Y (τ, w)
←−Q(τ) ,
where←−Y (τ, w) = {
←−Y i(τ, w)}.
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 9/15
Inhomogeneous differential equation
Introducing
←−Y i(τ, w) = Pr(
←−B (τ) ≤ w,
←−Z (τ) = i)
we can apply the analysis approach available forinhomogeneous MRMs.
It is based on the solution of the inhomogeneous partialdifferential equation
∂
∂τ
←−Y (τ, w) +
∂
∂w
←−Y (τ, w)R =
←−Y (τ, w)
←−Q(τ) ,
where←−Y (τ, w) = {
←−Y i(τ, w)}.
But a drawback of this approach is that it requires thecomputation of
←−Q(τ).
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 10/15
Homogeneous differential equation
To overcome this drawback we introduce the conditionaldistribution of reward accumulated by the reverse process
←−V i(τ, w) = Pr(
←−B (τ) ≤ w |
←−Z (τ) = i)
and the row vector←−V (τ, w) = {
←−V i(τ, w)}.
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 10/15
Homogeneous differential equation
To overcome this drawback we introduce the conditionaldistribution of reward accumulated by the reverse process
←−V i(τ, w) = Pr(
←−B (τ) ≤ w |
←−Z (τ) = i)
and the row vector←−V (τ, w) = {
←−V i(τ, w)}.
Using this performance measure we have to solve
∂
∂τ
←−V (τ, w) +
∂
∂w
←−V (τ, w)R =
←−V (τ, w)QT ,
where QT is the transpose of Q.
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 11/15
Block structure of the differential equation
Utilizing the special block structure of the Q′(τ) and the R′
matrices (of size 2#S) we can obtain two homogeneous partialdifferential equations of size #S:
∂
∂τ
←−X1(τ, w) +
∂
∂w
←−X1(τ, w)R =
←−X1(τ, w)QD ,
and
∂
∂τ
←−X2(τ, w)+
∂
∂w
←−X2(τ, w)Rα =
←−X1(τ, w)(Q−QD)T +
←−X2(τ, w)QT ,
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 12/15
Moments of accumulated reward
The analysis approach available for inhomogeneous MRMsallows to describe the moments of IMRMs with aninhomogeneous ordinary differential equation.
Similar to the reward distribution case, this approach is alsoapplicable for our model, but it requires the the computation of←−Q(τ).
Outline
Introduction
Model behaviour
Model description
Analysis approach
● Inhomogeneous differential
equation
● Homogeneous differential
equation
● Block structure of the
differential equation
● Moments of accumulated
reward
Numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 12/15
Moments of accumulated reward
The analysis approach available for inhomogeneous MRMsallows to describe the moments of IMRMs with aninhomogeneous ordinary differential equation.
Similar to the reward distribution case, this approach is alsoapplicable for our model, but it requires the the computation of←−Q(τ).
Using similar state dependent moment measures we obtainhomogeneous ordinary differential equations
d
dτ
←−−M1(n)(τ) = n
←−−M1(n−1)(τ)R +
←−−M1(n)(τ)QD ,
and
d
dτ
←−−M2(n)(τ) = n
←−−M2(n−1)(τ)Rα +
←−−M1(n)(τ)(Q−QD)T +
←−−M2(n)(τ)Q
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
● Randomization based
numerical method
Numerical example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 13/15
Randomization based numerical method
The ordinary differential equation with constant coefficientsallows to compose a randomization based numerical method.
←−−M1(n)(τ) = τn e RnED(τ) ,
and←−−M2(n)(τ) = n!dn
∞∑
k=0
e−λτ (λτ)k
k!D(n)(k),
where
D(n)(k) =
e (I−AkD) n = 0
0 k ≤ n, n ≥ 1
D(n−1)(k−1)Sα + D(n)(k−1)A+(
k−1n
)
e SnAk−1−nD (A−AD) k > n, n ≥ 1
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
● Numerical Example
Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 14/15
Numerical Example
rN=Nr rN−2=(N−2)rrN−1=(N−1)r r0 =0
rM =0
α N=0.5 α N−1=0.5 α N−2=0.5
α M
α 0
ρσ σ σ
σ
λλλ (N−1)N
0N−2N N−1
M
=1
=1
Structure of the Markov chain
1e-05
0.0001
0.001
0.01
0.1
1
10
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
1st moment2nd moment3rd moment4th moment5th moment
Moments of the accumulatedreward
With parameters N = 500000, λ = 0.000004, σ = 1.5, ρ = 0.1,r = 0.000002, α = 0.5,
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
■ non stopping time⇒ time reverse approach
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
■ non stopping time⇒ time reverse approach
■ inhomogeneous differential equation⇒ proper performancemeasure,
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
■ non stopping time⇒ time reverse approach
■ inhomogeneous differential equation⇒ proper performancemeasure,
■ partial differential equation⇒ ordinary differential equations,
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
■ non stopping time⇒ time reverse approach
■ inhomogeneous differential equation⇒ proper performancemeasure,
■ partial differential equation⇒ ordinary differential equations,
■ numerical stability, error control⇒ randomization basedanalysis.
Outline
Introduction
Model behaviour
Model description
Analysis approach
Numerical method
Numerical example
Conclusions
● Conclusions
M Telek, Markov Anniversary Meeting, June 2006. Analysis of Markov Reward Models with Partial Reward Loss - p. 15/15
Conclusions
The analysis of partial loss MRM is usually rather complex.
We propose an analysis method with the following features:
■ non stopping time⇒ time reverse approach
■ inhomogeneous differential equation⇒ proper performancemeasure,
■ partial differential equation⇒ ordinary differential equations,
■ numerical stability, error control⇒ randomization basedanalysis.
Thanks for your attention.