distributed-lyapunov.. - University of Southern California

Report
Stochastic optimization for power-aware
distributed scheduling
ω(t)
t
Michael J. Neely
University of Southern California
http://www-bcf.usc.edu/~mjneely
Outline
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
A single wireless device
R(t) = r(P(t), ω(t))
chosen
observed
Timeslots t = {0, 1, 2, …}
ω(t) = Random channel state on slot t
P(t) = Power used on slot t
R(t) = Transmission rate on slot t
(function of P(t), ω(t))
Example
R(t) = log(1 + P(t)ω(t))
chosen
observed
ω(t)
t
R(t)
t
Example
R(t) = log(1 + P(t)ω(t))
chosen
observed
ω(t)
t
R(t)
t
Example
R(t) = log(1 + P(t)ω(t))
chosen
observed
ω(t)
t
R(t)
t
Optimization problem
Maximize: R
Subject to: P ≤ c
Given:
• Pr[ω(t)=ω] = π(ω) , ω in {ω1, ω2, …, ω1000}
• p(t) in P = {p1, p2, …, p5}
• c = desired power constraint
Consider randomized decisions
• ω(t) in {ω1, ω2, …, ω1000}
• P(t) in P = {p1, p2, …, p5}
Pr[pk | ωi ] = Pr[P(t) = pk | ω(t)=ωi]
5
∑Pr[pk | ωi ] = 1
k=1
( for all ωi in {ω1, ω2, …, ω1000} )
Linear programming approach
Max: R
1000 5
Max: ∑ ∑ π(ωi) Pr[pk|ωi] r(pk,ωi)
i=1 k=1
S.t. : P ≤ c
S.t.:
1000 5
∑ ∑ π(ωi) Pr[pk|ωi] pk ≤ c
i=1 k=1
Given parameters:
π(ωi)
(1000 probabilities)
r(pk , ωi)
(5*1000 coefficients)
Optimization variables:
Pr[pk|ωi]
(5*1000 variables)
Multi-dimensional problem
R1(t)
1
R2(t)
Access
Point
RN(t)
2
• Observe (ω1(t), …, ωN(t))
• Decisions:
-- Choose which user to serve
-- Choose which power to use
N
Goal and LP approach
Maximize: R1 + R2 + … + RN
Subject to: Pn ≤ c for all n in {1, …, N}
LP has given parameters:
π(ω1, …, ωN) (1000N probabilities)
rn(pk , ωi)
(N*5N*1000N coefficients)
LP has optimization variables:
Pr[pk|ωi]
(5N*1000N variables)
Advantages of LP approach
• Solves the problem of interest
• LPs have been around for a long time
• Many people are comfortable with LPs
Disadvantages of LP approach
Disadvantages of LP approach
• Need to estimate an exponential
number of probabilities.
• LP has exponential number of variables.
• What if probabilities change?
• Fairness?
• Delay?
• Channel errors?
Lyapunov optimization approach
Maximize: R1 + R2 + … + RN
Subject to: Pn ≤ c for all n in {1, …, N}
Lyapunov optimization approach
Maximize: R1 + R2 + … + RN
Subject to: Pn ≤ c for all n in {1, …, N}
Virtual queue for each constraint:
Pn(t)
Qn(t)
c
Qn(t+1) = max[Qn(t) + Pn(t) – c, 0]
Stabilizing virtual queue  constraint satisfied!
Lyapunov drift
Q2
Q1
L(t) = ½ ∑ Qn(t)2
n
Δ(t) = L(t+1) – L(t)
Drift-plus-penalty algorithm
Every slot t:
• Observe (Q1(t), …., QN(t)), (ω1(t), …, ωN(t))
• Choose (P1(t), …, PN(t)) to greedily minimize:
Δ(t) - (1/ε)(R1(t) + … + RN(t))
drift
penalty
• Update queues.
Low complexity
No knowledge of π(ω) probabilities is required
Specific DPP implementation
• Each user n observes ωn(t), Qn(t).
• Each user n chooses Pn(t) in P to minimize:
-(1/ε)rn(Pn(t), ωn(t)) + Qn(t)Pn(t)
• Choose user n* with smallest such value.
• User n* transmits with power level Pn*(t).
Low complexity
No knowledge of π(ω) probabilities is required
Performance Theorem
Assume it is possible to satisfy the constraints.
Then under DPP with any ε>0:
• All power constraints are satisfied.
• Average thruput satisfies:
R1 + … + RN ≥ throughputopt – O(ε)
• Average queue size satisfies:
∑ Qn
≤ O(1/ε)
General SNO problem
ω(t) = Observed random event on slot t
π(ω) = Pr[ω(t)=ω] (possibly unknown)
α(t) = Control action on slot t
Aω(t) = Abstract set of action options
Minimize:
y0(α(t), ω(t))
Subject to:
yn(α(t), ω(t)) ≤ 0 for all n in {1, …, N}
α(t) in Aω(t) for all t in {0, 1, 2, …}
Such problems are solved by the DPP algorithm.
Performance theorem: O(ε), O(1/ε) tradeoff.
What we have done so far
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
What we have done so far
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
What we have done so far
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
What we have done so far
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
Mobile P2P video downloads
Mobile P2P video downloads
Mobile P2P video downloads
Access
Point
Mobile P2P video downloads
Access
Point
Mobile P2P video downloads
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Mobile P2P video downloads
Access
Point
Access
Point
Access
Point
Cache-aware scheduling
• Access points (including “femto” nodes)
• Typically stationary
• Typically have many files cached
• Users
• Typically mobile
• Typically have fewer files cached
• Assume each user wants one “long” file
• Can opportunistically grab packets from
any nearby user or access point that has
the file.
Quality-aware video delivery
Quality Bits: 40968 Bits:
277256
Layer L D: 0
Bits: 419640 Bits: 299216
D: 0
D: 0
D: 0
Quality
Layer 2
Quality
Layer 1
Bits: 7370
D: 10.777
Bits: 41304 Bits: 72800
D: 6.716
D: 6.261
Bits: 59984
D: 6.129
Bits: 8176
D: 11.045
Bits: 58152
D: 7.363
Bits: 97864
D: 6.971
Bits:
120776
D: 7.108
Video chunks as time progresses
• D = Distortion.
• Results hold for any matrices Bits(layer, chunk), D(layer, chunk).
• Bits are queued for wireless transmission.
Fair video quality delivery
Minimize: f( D1 ) + f( D2 ) + … + f( DN )
Subject to:
Pn ≤ c for all n in {1, …, N}
Video playback rate constraints
Fair video quality delivery
Minimize: f( D1 ) + f( D2 ) + … + f( DN )
Subject to:
Pn ≤ c for all n in {1, …, N}
Video playback rate constraints
Recall the general form:
Min: y0
S.t. : yn ≤ 0 for all n
α(t) in Aω(t) for all t
Fair video quality delivery
Minimize: f( D1 ) + f( D2 ) + … + f( DN )
Subject to:
Pn ≤ c for all n in {1, …, N}
Video playback rate constraints
Recall the general form:
Min: y0
S.t. : yn ≤ 0 for all n
α(t) in Aω(t) for all t
Define Yn(t) = Pn(t) - c
Fair video quality delivery
Minimize: f( D1 ) + f( D2 ) + … + f( DN )
Subject to:
Pn ≤ c for all n in {1, …, N}
Video playback rate constraints
Recall the general form:
Min: y0
S.t. : yn ≤ 0 for all n
α(t) in Aω(t) for all t
Define auxiliary variable
γ(t) in [0, Dmax]
Equivalence via Jensen’s inequality
Minimize: f( D1 ) + f( D2 ) + … + f( DN )
Subject to:
Pn ≤ c for all n in {1, …, N}
Video playback rate constraints
Minimize: f( γ1(t)) + f( γ2(t)) + … + f( γN(t))
Subject to:
Pn ≤ c for all n in {1, …, N}
γn = Dn for all n in {1, …, N}
Video playback rate constraints
Example simulation
BS
• Region divided into 20 x 20 subcells
(only a portion shown here).
• 1250 mobile devices, 1 base station
• 3.125 mobiles/subcell
•
•
•
•
Phases 1, 2, 3: File availability prob = 5%, 10%, 7%
Basestation Average Traffic: 2.0 packets/slot
Peer-to-Peer Average Traffic: 153.7 packets/slot
Factor of 77.8 gain compared to BS alone!
What we have done so far
• Lyapunov optimization method
• Power-aware wireless transmission
– Basic problem
– Cache-aware peering
– Quality-aware video streaming
• Distributed sensor reporting and correlated
scheduling
Distributed sensor reports
ω1(t)
1
ω2(t)
2
Fusion
Center
• ωi(t) = 0/1 if sensor i observes the event on slot t
• Pi(t) = 0/1 if sensor i reports on slot t
• Utility: U(t) = min[P1(t)ω1(t) + (1/2)P2(t)ω2(t),1]
Maximize:
U
Subject to:
P1 ≤ c
P2 ≤ c
What is optimal?
Agree
on plan 0 1 2 3 4
t
What is optimal?
Agree
on plan 0 1 2 3 4
Example plan:
User 1:
• t=even  Do not report.
• t=odd  Report if ω1(t)=1.
User 2:
• t=even  Report if ω2(t)=1
• t=odd:  Report with prob ½ if ω2(t)=1
t
Common source of randomness
Day 1
Day 2
Example: 1 slot = 1 day
Each user looks at Boston Globe every day:
• If first letter is a “T”  Plan 1
• If first letter is an “S”  Plan 2
• Etc.
Specific example
Assume:
• Pr[ω1(t)=1] = ¾, Pr[ω2(t)=1] = ½
• ω1(t), ω2(t) independent
• Power constraint c = 1/3
Approach 1: Independent reporting
• If ω1(t)=1, user 1 reports with probability θ1
• If ω2(t)=1, user 2 reports with probability θ2
Optimizing θ1, θ2 gives u = 4/9 ≈ 0.44444
Approach 2: Correlated reporting
Pure strategy 1:
• User 1 reports if and only if ω1(t)=1.
• User 2 does not report.
Pure strategy 2:
• User 1 does not report.
• User 2 reports if and only if ω2(t)=1.
Pure strategy 3:
• User 1 reports if and only if ω1(t)=1.
• User 2 reports if and only if ω2(t)=1.
Approach 2: Correlated reporting
X(t) = iid random variable (commonly known):
• Pr[X(t)=1] = θ1
• Pr[X(t)=2] = θ2
• Pr[X(t)=3] = θ3
On slot t:
• Users observe X(t)
• If X(t)=k, users use pure strategy k.
Optimizing θ1, θ2, θ3 gives u = 23/48 ≈ 0.47917
Summary of approaches
Strategy
u
Independent reporting
0.44444
Correlated reporting
0.47917
Centralized reporting
0.5
Summary of approaches
Strategy
u
Independent reporting
0.44444
Correlated reporting
0.47917
Centralized reporting
0.5
It can be shown that this is optimal over all
distributed strategies!
General distributed optimization
Maximize:
U
Subject to: Pk ≤ 0 for k in {1, …, K}
ω(t) = (ω1(t), …, ωΝ(t))
π(ω) = Pr[ω(t) = (ω1, …, ωΝ)]
α(t) = (α1(t), …, αΝ(t))
U(t) = u(α(t), ω(t))
Pk(t) = pk(α(t), ω(t))
Pure strategies
A pure strategy is a deterministic vectorvalued function:
g(ω) = (g1(ω1), g2(ω2), …, gΝ (ωΝ))
Let M = # pure strategies:
M = |A1||Ω1| x |A2||Ω2| x ... x |AN||ΩN|
Optimality Theorem
There exist:
• K+1 pure strategies g(m)(ω)
• Probabilities θ1, θ2, …, θK+1
such that the following distributed
algorithm is optimal:
X(t) = iid, Pr[X(t)=m] = θm
• Each user observes X(t)
• If X(t)=m  use strategy g(m)(ω).
LP and complexity reduction
•
The probabilities can be found by an LP
•
Unfortunately, the LP has M variables
•
If (ω1(t), …, ωΝ(t)) are mutually independent
and the utility function satisfies a preferred
action property, complexity can be reduced
•
Example N=2 users, |A1|=|A2|=2
--Old complexity = 2|Ω1|+|Ω2|
--New complexity = (|Ω1|+1)(|Ω2|+1)
Lyapunov optimization approach
•
Define K virtual queues Q1(t), …, QK(t).
•
Every slot t, observe queues and choose
strategy m in {1, …, M} to maximize a
weighted sum of queues.
•
Update queues with delayed feedback:
Qk(t+1) = max[Qk(t) + Pk(t-D), 0]
Separable problems
If the utility and penalty functions are a
separable sum of functions of individual
variables (αn(t), ωn(t)), then:
•
There is no optimality gap between
centralized and distributed algorithms
•
Problem complexity reduces from
exponential to linear.
Simulation (non-separable problem)
•
•
•
•
•
•
3-user problem
αn(t) in {0, 1} for n ={1, 2, 3}.
ωn(t) in {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
V=1/ε
Get O(ε) guarantee to optimality
Convergence time depends on 1/ε
Utility
Utility versus V parameter (V=1/ε)
V (recall V = 1/ε)
Average power up to time t
Average power versus time
V=100
V=50
V=10
power constraint 1/3
Time t
Adaptation to non-ergodic changes
Conclusions
• Drift-plus-penalty is a strong technique for
general stochastic network optimization
• Power-aware scheduling
• Cache-aware scheduling
• Quality-aware video streaming
• Correlated scheduling for distributed
stochastic optimization
Conclusions
• Drift-plus-penalty is a strong technique for
general stochastic network optimization
• Power-aware scheduling
• Cache-aware scheduling
• Quality-aware video streaming
• Correlated scheduling for distributed
stochastic optimization

similar documents