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

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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

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

1/30


Challenges

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

2/30


Goal of the paper

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

3/30


Client server model

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

slot length = transmission duration

2

1

AP

3

4/30


Channel model

Channel Model

  • Unreliable, non-homogeneous wireless channels

    • successful with probability pn

    • failed with probability 1-pn

    • p1,p2,…,pN may be different

2

p2

1

p1

AP

p3

3

5/30


Uplink protocol

Uplink Protocol

  • Poll (ex. CF-POLL in 802.11 PCF)

  • Data

  • No need for ACK

  • pn = Prob( both Poll/Data are delivered)

Data

2

p2

1

p1

POLL

AP

p3

3

6/30


Downlink protocol

Downlink Protocol

  • Data

  • ACK

  • pn = Prob( both Data/ACK are delivered)

ACK

2

p2

1

p1

Data

AP

p3

3

7/30


Traffic model

Traffic Model

  • Group time slots into intervals with τ time slots

  • Clients may generate packets at the beginning of each interval

{1,.,3}

{.,2,.}

{1,2,3}

τ

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

1

p1

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

3

8/30


Delay bound

Delay Bound

  • Deadline = Interval

  • Packets are dropped if not delivered by the deadline

  • Delay of successful delivered packet is at most τ

{1,.,3}

{.,2,.}

{1,2,3}

τ

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

1

p1

AP

arrival

deadline

p3

{1,.,3}

{.,2,.}

{1,2,3}

3

9/30


Packet scheduling

Packet Scheduling

{1,.,3}

{.,2,.}

{1,2,3}

forced idleness

F

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

S

I

I

1

p1

dropped

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

F

S

3

10/30


Timely throughput

Timely Throughput

  • Timely throughput = avg. # of delivered packets per interval

{1,.,3}

{.,2,.}

{1,2,3}

F

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

S

I

I

1

p1

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

F

S

3

11/30


Packet arrivals

Packet Arrivals

  • Distribution of packet arrivals is specified

{1,.,3}

{.,2,.}

{1,2,3}

F

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

S

I

I

1

p1

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

F

S

3

12/30


Qos requirements

QoS Requirements

  • Client n requires timely throughput qn

  • Delivery ratio requirement of client n

    = qn /{arrival prob. of client n}

{1,.,3}

{.,2,.}

{1,2,3}

F

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

S

I

I

1

p1

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

F

S

3

13/30


Problem formulation

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

14/30


Work load

Work Load

  • The proportion of time slots needed for client n is

15/30


Work load1

Work Load

  • The proportion of time slots needed for client n is

expected number of time slots needed for a successful transmission

15/30


Work load2

Work Load

  • The proportion of time slots needed for client n is

number of required successful transmissions in an interval

15/30


Work load3

Work Load

  • The proportion of time slots needed for client n is

normalize by interval length

15/30


Work load4

Work Load

  • The proportion of time slots needed for client n is

  • We call wn the “work load”

15/30


Necessary condition for feasibility

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

{1,.,3}

{.,2,.}

{1,2,3}

F

{1,2,3}

{1,.,3}

{.,2,.}

2

p2

S

I

I

1

p1

AP

p3

{1,.,3}

{.,2,.}

{1,2,3}

F

S

3

16/30


Stronger necessary condition

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

17/30


Largest debt first scheduling policies

Largest Debt First Scheduling Policies

  • Give higher priority to client with higher “debt”

{1,2,3}

F

F

S

{1,2,3}

2

p2

F

1

p1

AP

p3

{1,2,3}

F

S

3

18/30


Two definitions of debt

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

19/30


Evaluation methodology

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

20/30


Evaluated applications

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

21/30


Voip traffic

VoIP Traffic

  • ITU-T G.729.1

    • Bit rates between 8 kb/s to 32 kb/s

    • Different bit rates correspond to different periods

22/30


Voip clients

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

23/30


Voip results a feasible set

VoIP Results: A Feasible Set

24/30


Voip results a feasible set1

VoIP Results: A Feasible Set

fulfilled

24/30


Voip results a feasible set2

VoIP Results: A Feasible Set

24/30


Voip results a feasible set3

VoIP Results: A Feasible Set

24/30


Voip results a feasible set4

VoIP Results: A Feasible Set

24/30


Voip results an infeasible set

VoIP Results: An Infeasible Set

25/30


Voip results an infeasible set1

VoIP Results: An Infeasible Set

small shortfall

25/30


Voip results an infeasible set2

VoIP Results: An Infeasible Set

25/30


Voip results an infeasible set3

VoIP Results: An Infeasible Set

25/30


Voip results an infeasible set4

VoIP Results: An Infeasible Set

25/30


Mpeg traffic

MPEG Traffic

  • Model MPEG VBR traffic by a Markov chain consisting of three activity states (Martin et al)

  • MAC: 802.11a

26/30


Mpeg clients

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

27/30


Mpeg results a feasible set

MPEG Results: A Feasible Set

28/30


Mpeg results a feasible set1

MPEG Results: A Feasible Set

fulfilled

28/30


Mpeg results a feasible set2

MPEG Results: A Feasible Set

28/30


Mpeg results a feasible set3

MPEG Results: A Feasible Set

28/30


Mpeg results a feasible set4

MPEG Results: A Feasible Set

28/30


Mpeg results an infeasible set

MPEG Results: An Infeasible Set

29/30


Mpeg results an infeasible set1

MPEG Results: An Infeasible Set

small shortfall

29/30


Mpeg results an infeasible set2

MPEG Results: An Infeasible Set

29/30


Mpeg results an infeasible set3

MPEG Results: An Infeasible Set

29/30


Mpeg results an infeasible set4

MPEG Results: An Infeasible Set

29/30


Conclusion

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

30/30


I hong hou p r kumar

Thank You


Backup slides

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


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