I-Hong Hou P.R. Kumar

Admission Control and Scheduling for QoS Guarantees for Variable-Bit-Rate Applications on Wireless Channels

I-Hong Hou

P.R. Kumar

University of Illinois,

Urbana-Champaign

Background: Wireless Networks

There will be increasing use of wireless networks for serving traffic with QoS constraints:

VoIP

Video Streaming

Real-time Monitoring

Networked Control

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Challenges Wireless Network limitation

Non-homogeneous, unreliable wireless links Client QoS requirements

Specified traffic pattern Delay bound Delivery ratio bound Throughput bound

System perspective Fulfill clients with different QoS requirements

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Goal of the Paper Prior work [Hou, Borkar, and Kumar]:

All clients generate traffic with the same rate Admission control and packet scheduling policies

Q: How to deal with more complicated traffic patterns? Applications with variable-bit-rate (VBR) traffic

MPEG streaming Clients generate traffic with different rates

This work extends results to arbitrary traffic patterns

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Client-Server Model A system with N wireless clients and one AP Time is slotted One packet transmission in each slot AP schedules all transmissions

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AP1

2

slot length = transmission duration

3

Channel Model Unreliable, non-homogeneous wireless channels

successful with probability pn

failed with probability 1-pn

p1,p2,…,pN may be different

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AP1

2p1p2

3

p3

Uplink Protocol Poll (ex. CF-POLL in 802.11 PCF) Data No need for ACK pn = Prob( both Poll/Data are delivered)

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AP1

2p1p2POLL

Data

3

p3

Downlink Protocol Data ACK pn = Prob( both Data/ACK are delivered)

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AP1

2p1p2Data

ACK

3

p3

Traffic Model Group time slots into intervals with τ time slots Clients may generate packets at the beginning of

each interval

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AP1

2

3

p1p2

p3

τ{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

Delay Bound Deadline = Interval Packets are dropped if not delivered by the deadline Delay of successful delivered packet is at most τ

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AP1

2

3

p1p2

p3

{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

τ

arrival deadline

S I

Packet Scheduling

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AP1

2

3

p1p2

p3

SF

F

I

forced idleness{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

dropped

S I

Timely Throughput Timely throughput = avg. # of

delivered packets per interval

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AP1

2

3

p1p2

p3

SF

F

I

Client # Throughput

1 0

2 0.5

3 0.5

{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

S I

Packet Arrivals Distribution of packet

arrivals is specified

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AP1

2

3

p1p2

p3

SF

F

I

{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

Arrival Proportion of Occurrences

{1,3} 1/3

{2} 1/3

{1,2,3} 1/3

S I

QoS Requirements Client n requires timely throughput qn

Delivery ratio requirement of client n

= qn /{arrival prob. of client n}

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AP1

2

3

p1p2

p3

SF

F

I

{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

Client # Delivery ratio

1 0

2 1

3 1

Problem Formulation Admission control

Given τ, packet arrivals, pn, qn, decide whether a set of clients is feasible

Scheduling policy Design a policy that fulfills every feasible set of

clients

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The proportion of time slots needed for client n is

Work Load

1 nn

n

qw

p

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Work Load

1 nn

n

qw

p

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expected number of time slots needed for a successful transmission


Work Load

1 nn

n

qw

p

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number of required successful transmissions in an interval


Work Load

1 nn

n

qw

p

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normalize by interval length


We call wn the “work load”

Work Load

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1 nn

n

qw

p

S I

Necessary Condition for Feasibility Necessary condition from classical queuing theory: But the condition is not sufficient Packet drops by deadline misses cause more idleness than in

queuing theory

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AP1

2

3

p1p2

p3

SF

F

I

11

N

nnw

{1,.,3}

{1,.,3}

{1,.,3} {.,2,.}

{.,2,.}

{.,2,.}

{1,2,3}

{1,2,3}

{1,2,3}

Stronger Necessary Condition Let IS = Expected proportion of the idle time when

the server only works on S IS decreases as S increases

Theorem: the condition is both necessary and sufficient

Admission control checks the condition

1, {1,2,..., }n Sn S

w I S N

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Largest Debt First Scheduling Policies

Give higher priority to client with higher “debt”

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AP1

2

3

p1p2

p3

{1,2,3}

{1,2,3}

{1,2,3}F F S

F S

F

Two Definitions of Debt The time debt of client n

time debt = wn – actual proportion of transmission time given to client n

The weighted delivery debt of client n weighted delivery debt = (qn – actual timely throughput)/pn

Theorem: Both largest debt first policies fulfill every feasible set of clients Feasibility Optimal Policies

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Evaluation Methodology

Evaluate five policies: DCF Enhanced DCF (EDCF) by 802.11e PCF with randomly assigned priorities (random) Time debt first policy Weighted-delivery debt first policy

Metric: Shortfall in Timely Throughput

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Evaluated Applications VoIP

Generate packets periodically Duplex traffic Clients may generate packets by different period

MPEG Generate packets probabilistically Only downstream traffic Clients may generate packets by different probability

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VoIP Traffic ITU-T G.729.1

Bit rates between 8 kb/s to 32 kb/s Different bit rates correspond to different periods

8kb/s – 32 kb/s bit rates 20 ms interval length

160 Byte packet 11 Mb/s transmission rate

610 μs time slot 32 time slots in an interval

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VoIP Clients Two groups of clients:

Feasible set: 6 group A clients, 5 group B clients Infeasible set: 6 group A clients, 6 group B clients

Group A Group B

60 ms (3 intervals) period 40 ms (2 intervals) period

21.3 kb/s traffic 32 kb/s traffic

require 99% delivery ratio require 80% delivery ratio

Starting times evenly spaced

Channel reliabilities range from 61% to 67%

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VoIP Results: A Feasible Set

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0

1

2

3

4

5

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut Weighted-

delivery

0

1

2

3

4

5

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut Weighted-

delivery

Time-based


fulfilled

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0

1

2

3

4

5

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

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hp

ut Weighted-

delivery

Time-based

Random


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0

1

2

3

4

5

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

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hro

ug

hp

ut Weighted-

delivery

Time-based

Random

DCF


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0

1

2

3

4

5

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

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hro

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hp

ut Weighted-

delivery

Time-based

Random

DCF

EDCF

VoIP Results: An Infeasible Set

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0

1

2

3

4

5

6

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

0

1

2

3

4

5

6

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based


small shortfall

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0

1

2

3

4

5

6

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

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hro

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hp

ut

Weighted-delivery

Time-based

Random


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0

1

2

3

4

5

6

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based

Random

DCF


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0

1

2

3

4

5

6

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based

Random

DCF

EDCF

MPEG Traffic Model MPEG VBR traffic by a Markov chain

consisting of three activity states (Martin et al)

MAC: 802.11a

6 ms interval length 1500 Bytes packet

54 Mb/s transmission rate 9 time slots in an interval

Activity Great High Regular

Arrival probability 1 0.8 0.75

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MPEG Clients Two groups of clients

Group A generates traffic according to Martin et al and requires 90% delivery ratio

Group B generates traffic half as often as A and requires 80% delivery ratio

The nth client in each group has (60+n)% channel reliability

Feasible set: 4 group A clients, 4 group B clients Infeasible set: 5 group A clients, 4 group B clients

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MPEG Results: A Feasible Set

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0

0.2

0.4

0.6

0.8

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

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hro

ug

hp

ut Weighted-

delivery

0

0.2

0.4

0.6

0.8

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut Weighted-

delivery

Time-based


fulfilled

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0

0.2

0.4

0.6

0.8

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

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hp

ut Weighted-

delivery

Time-based

Random


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0

0.2

0.4

0.6

0.8

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

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hro

ug

hp

ut Weighted-

delivery

Time-based

Random

DCF


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0

0.2

0.4

0.6

0.8

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut Weighted-

delivery

Time-based

Random

DCF

EDCF

MPEG Results: An Infeasible Set

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0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

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hro

ug

hp

ut

Weighted-delivery

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based


small shortfall

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0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

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hro

ug

hp

ut

Weighted-delivery

Time-based

Random


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0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based

Random

DCF


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0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Time (sec)

Sh

ort

fall

in T

imel

y T

hro

ug

hp

ut

Weighted-delivery

Time-based

Random

DCF

EDCF

Conclusion Extend a framework for QoS to deal with traffic

patterns, deadlines, throughputs, delivery ratios, and channel unreliabilities

Characterize when QoS is feasible

Provide efficient scheduling policies

Address implementation issues

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Backup Slides An example:

Two clients, τ = 3 p1=p2=0.5 q1=0.876, q2=0.45 w1=1.76/3, w2=0.3 I{1}=I{2}=1.25/3, I{1,2}=0.25/3

w1+I{1}=3.01/3 > 1 However, w1+w2+I{1,2}=2.91/3 < 1

Documents

I-Hong Hou P.R. Kumar