D. Wu & R. Negi - CSE SERVICESranger.uta.edu/~li/2-24-lecture.pdf · D. Wu & R. Negi IEEE Trans. On...
Transcript of D. Wu & R. Negi - CSE SERVICESranger.uta.edu/~li/2-24-lecture.pdf · D. Wu & R. Negi IEEE Trans. On...
1
Effective Capacity: A Wireless Link Model for Support of Quality of Service
D. Wu & R. Negi
IEEE Trans. On Wireless Communications vol. 2 no. 4, July, 2003
2
OutlineOutline•• Motivation of this paperMotivation of this paper•• Brief review of Brief review of Effective BandwidthEffective Bandwidth•• New model used at upper layer to New model used at upper layer to
capture capture wireless channel qualitywireless channel quality•• SimulationSimulation•• ContributionContribution & & LimitationsLimitations
3
QoS Provisioning in Wireless Networks Source
Receiver
Wireless network
How do we guarantee How do we guarantee QoSQoS to the user?to the user?
Streaming video
Quality of Service (QoS)•Data rate •Delay tolerable •Packet loss probability
RmaxD
lossPR
})({Prob maxDtDPloss ≥=noise, interference
4
})({Prob maxDtDPloss ≥=
Key issue: time varying wireless channel
How do we estimate wireless channel capacity?How do we estimate wireless channel capacity?
Receiver moves
Power fluctuates
Data capacity changes
Packets get loss
5
Need for a novel wireless channel model•“Radio layerRadio layer” channel models represent power fluctuationspower fluctuations
•Hard/impossible to extract QoSQoS metricsmetrics }P,D{R, lossmax
Markov model?Doppler spectrum?AR model?
Rayleigh fading?Gans spectrum?Step 1Step 1: Estimate
model parameters
Date rate (R)? Delay tolerable (D)? Packet loss probability (P)?
Step 2Step 2: Map estimated model to QoS metrics
6
A “link-layer” channel model
Model as timeModel as time--varying servervarying server
What is channel “capacity”, when QoS is accounted for?
link-level channel model Easily computes delay
constrained capacity
7
Motivation of This PaperMotivation of This Paper•• Model wireless channel in terms of Model wireless channel in terms of upperupper--
layer layer QoSQoS metricsmetrics such as such as data rate, delay, data rate, delay, and delayand delay--violation probabilityviolation probability to to facilitate facilitate support of support of QoSQoS in nextin next--generation wireless generation wireless networksnetworks– easy of translation into QoS guarantees– simplicity of implementation– accuracy
8
Main IdeaMain Idea
Statistical Service Curve Statistical Service Curve
+Effective Bandwidth Theory Effective Bandwidth Theory
New Channel Capacity Model
9
What is Effective What is Effective Bandwidth ?Bandwidth ?Data RateData Rate
TimeTime
Peak ratePeak rate
Average rateAverage rate
Traffic RateTraffic Rate
Effective Bandwidth was proposed by Gibbens& Hunt, Guerin, and Kelly in 1991
Minimum data rate needed for arrival traffic to meet its QoS requirements
10
Definition of Effective BandwidthDefinition of Effective Bandwidth•• A(t) ( total arrival traffic during [0,t) ) has A(t) ( total arrival traffic during [0,t) ) has
stationary incrementsstationary increments
is called as effective bandwidth of arrival traffic
•• Range:Range:
)[0,tθ, ]logE[eθt1t)α(θ, θA(t) ∞∈=
t)α(θ,
∞→→→→
≤≤
θ as ratepeak t)α(θ,0θ as rate average t)α(θ,
ratepeak t)α(θ, rate average
11
Effective Bandwidth & Effective Bandwidth & QoSQoS
ε D} P{delay ≤≥
θ
Data RateData RatePeak ratePeak rate
Average rateAverage rate
),(lim)( tt
θαθα∞→
=
Large
Small
Small
θ
θ)α(t,
Small
Large
Large
ε
θθ)α(t,
12
ExamplesExamples•• Regulated TrafficRegulated Traffic
•• OnOn--Off TrafficOff Traffic
•• FBM TrafficFBM Traffic
ττ
ττ
ττ τ
)(Alimρ where
1)](e)(A
ρlog[1θ1)α(θ,
*
t
)(θA*
*
∞→=
−+=
))1(1log(1),( −+= θ
θτθα Pe
Pr
ON Off1-r/Pr/P
1-r/Pr/P
A*( )
A(t+ )-A(t)<A*( )
(0,1)HParamter Hurst ,tEZ 3.
0EZ 2. 0. Z1.rate icmean traffr βZrtA(t)
2H2t
t0
t
∈=
===+=
12
21),( −+= Hr βθττθα
τττ
13
Extended Definition of Effective Extended Definition of Effective BandwidthBandwidth
•• For a stochastic process {For a stochastic process {X(t),tX(t),t>0} with >0} with stationary and nonnegative incrementsstationary and nonnegative increments
is called as effective bandwidth of the stochastic process
)[0,t),,(- ],logE[etθ
1t),(α θX(t)X ∞∈∞∞∈= θθ
t),(αX θ
14
Effective Bandwidth & Effective Bandwidth & QoSQoS
A(t)=A1(t)+A2(t)+…+An(t)A(t)=A1(t)+A2(t)+…+An(t)
A1(t)A1(t)
Ai(t)Ai(t)
An(t)An(t) FIFOFIFO
Q(t)Q(t)C(tC(t))
s)](C(t)A(s)[A(t)maxQ(t)ts0
C+−−=≤≤
x]P[Q(t)supx]P[Qt
≥=≥
∗∗
∞→≈≥⇔−≤≥
⇓∞→→+
θγθ
θαθα
x-ex}P{Q x}logP{Qx1lim
tas 0t),(-t),(
x
**A C
•• Assume aggregated traffic Assume aggregated traffic arrival process arrival process A(tA(t) and link ) and link capacity process capacity process C(tC(t) are ) are stationary, thenstationary, then
15
Special CasesSpecial CasesrtC −=− ),( θα
A(t)=A1(t)+A2(t)+…+An(t)A(t)=A1(t)+A2(t)+…+An(t)
A1(t)A1(t)
Ai(t)Ai(t)
An(t)An(t) FIFOFIFO
Q(t)Q(t)C(tC(t))
s)](C(t)A(s)[A(t)maxQ(t)ts0
C+−−=≤≤
x]P[Q(t)supx]P[Qt
≥=≥
),(lim)( ]0)(Pr[)()(x]Pr[D(t)sup )(
t
1
tgtDrer
At
xrgr
θαθγ
γ
∞→
×−
=≥=
×≈≥−
utA =),(θα
),(lim)( ]0)(Pr[)()(x]Pr[D(t)sup )(
t
1
thtDueu
Ct
xuhu
θαθγ
γ
−−=≥=
×≈≥
∞→
×− −
C(t)=r*t è
A(t)=u*t è
16
is called linkis called link--layer wireless channel modellayer wireless channel model)}(),({ 1 uhu −γ
Proposed Link-level channel model
•• According toAccording to:
• Interpretation– Probability of delay, experienced by CBR
traffic with rate u, more than D is bounded by
max1 )()( Duhueu ××− −
×γ
max1 )(
max )(})(Pr{sup Duhu
teuDtD ××− −
×≈≥ γ
17
Simple Estimation AlgorithmSimple Estimation Algorithmmax
1 )(u max )(})(Pr{ DuheuDtD ××− −
×≈≥ γ
[ ])(
)()( 1 uhuutDE −×
=γ [ ] [ ] uEutDE s /Q(t))()( +=τ [ ]Q(t))(
)()(1
Euuuuh
s +×=−
τγ
•• Taking N samples over an interval with length TTaking N samples over an interval with length T– S(n) indicates whether a packet is in service at the n-th sample– Q(n) indicates queue length at the n-th sample– T(n) indicates the remaining service time of the packet at the n-th sample
Q)(h )(1 )(1Q )(1 1-
1s
11 +×==== ∑∑∑
=== s
N
n
N
n
N
n uunT
NnQ
NnS
N τγ
τγ
max1 )(
max })(Pr{ DuhueDtD ××− −
×≈≥ γ
average remaining time of a packet being served
18
Summary of EC Link ModelSummary of EC Link Model
)}(),({ 1 uhu −γ
)}(),({ 1 uhu −γ
)(tC),(lim)( th Ct
θαθ −=∞→
20
Accuracy
Rayleigh fading channelSNRavg = 15 dBrawgn =100 kb/sfm =30 Hz
Traffic rate = 85 kb/s
Traffic rate = 80 kb/s
Traffic rate = 75 kb/s rawgn=B*log(1+SNRavg)
21
Rayleigh fading channel SNRavg = 0 dBrawgn =100 kb/sfm =30 Hz
Accuracy
Traffic rate = 85 kb/s
Traffic rate = 80 kb/s
Traffic rate = 75 kb/s
22
Rayleigh fading channel
SNRavg = 15 dBrawgn =100 kb/sfm = 5 Hz
Accuracy
Traffic rate = 80 kb/s
Traffic rate = 85 kb/s
Traffic rate = 75 kb/s
23
Contribution of This PaperContribution of This Paper
max1 )(
max )(})(Pr{sup Duuh
teuDtD ×− −
×≈≥ γ
linklink--layer wireless channel modellayer wireless channel model )}(),({ 1 uhu −γ
for QoS Guarantees
24
Limitation of This PaperLimitation of This Paper•• Not solid theoretical foundationNot solid theoretical foundation
•• Are the algorithms based on sampling accurate and simple? Are the algorithms based on sampling accurate and simple?
•• Are the algorithm easy to use?Are the algorithm easy to use?•• Only for CBR trafficOnly for CBR traffic•• Does not explicitly take modulation and channel coding into accoDoes not explicitly take modulation and channel coding into accountunt
Q)(h )(1 )(1Q )(1 1-
1s
11 +×==== ∑∑∑
=== s
N
n
N
n
N
n uunT
NnQ
NnS
N τγ
τγ
∗∗
∞→≈≥⇔−≤≥
⇓∞→→+
θγθ
θαθα
x-ex}P{Q x}logP{Qx1lim
tas 0t),(-t),(
x
**A C
max1 )(
max})(Pr{ DuhueDtD ××− −
×≈≥ γ
25
Future WorkFuture Work•• More deeply use effective bandwidth More deeply use effective bandwidth
theorytheory•• Take its advantage but avoid it Take its advantage but avoid it
disadvantagedisadvantage•• Build a more power channel capacity model Build a more power channel capacity model
for for QoSQoS provisioningprovisioning•• Implementation in 3G or 4GImplementation in 3G or 4G