Scheduling Heterogeneous Real- Time Traffic over Fading Wireless Channels I-Hong Hou P.R. Kumar...

Scheduling Heterogeneous Real-Time Traffic over Fading Wireless Channels

I-Hong Hou

P.R. Kumar

University of Illinois,

Urbana-Champaign

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Background: Wireless Networks

There will be increasing use of wireless networks for serving traffic with QoS constraints: Example: VoIP, Video Streaming, Real-time Monitoring,

Networked Control, etc.

Client requirements include Specified traffic patterns Delay bounds Timely throughput bounds

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Previous Work and Challenges Prior work [Hou et al] [Hou and Kumar]:

Clients have hard throughput requirements Static but unreliable wireless channels All clients require the same delay bounds Optimal packet scheduling policies are proposed

Q: How to deal with more complicated scenarios? Rate adaptation may be applied Channel qualities can be time-varying Clients may require different delay bounds

This work extends the model in prior work and proposes a guideline for these scenarios

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Client-Server Model A system with N wireless clients and one AP Time is slotted AP schedules all transmissions

AP1

2

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More General Traffic Model Group time slots into periods with T time slots Clients may generate packets at the beginning of

each period

AP1

2

3

{1,.,3}

{.,2,.}

{1,2,3}

{1,.,3}

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

{.,2,.}

{1,2,3}

{1,2,3}

T

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Different Delay Bounds Deadline for client n = τn

AP1

2

3

τ1=4arrival deadline

τ3=3deadlinearrival

τ2=5deadlinearrival

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Without Rate Adaptation Transmission takes 1

time slot Transmissions succeeds

with probability pc,n

Channel Model Channel changes from period to period Channels are static within a period System may or may not support rate adaptation

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With Rate Adaptation Transmission takes sc,n

time slots under channel c

Transmissions are error-free

Timely Throughput Requirements Timely throughput

=

Client n requires timely throughput qn

Q: How to design a scheduling policy to fulfill requirements of all feasible sets of clients? Feasibility optimal scheduling policy

# of delivered packets

# of periods

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Pseudo-debt Delivery debt: deficiency of timely throughput

Time debt: deficiency of time spent on a client

Pseudo-debt rn(t) quantifies the behavior of client n up to time t

The set of clients is fulfilled converges to 0 in probability

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( )[ ]nr t

t

( / ) # of packets delivered for client nt T q n

Sufficient Condition for Optimality Let μn be the reduction on debt for client n

Theorem:

A policy that maximizes for each period is feasibility optimal.

Analogous to Max-Weight scheduling in wireline networks

{ ( ) }n nn

E r t

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Rate Adaptation with Different Delay Bounds Scenario:

Rate adaptation used Clients may have different per packet delay bounds, τn

Modified Knapsack Policy: Find an ordered set S={m1,m2,…} to maximize total debt

A variation of knapsack problem and can be solved by DP

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τ1=4 τ2=7 τ3=10S1 = 3

S2 = 5

S3 = 4S1 = 3 S3 = 4





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S3 = 4S2 = 5

τ1=4 τ2=7 τ3=10S1 = 3

S2 = 5

S3 = 4





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S2 = 5S1 = 3

τ1=4 τ2=7 τ3=10S1 = 3

S2 = 5

S3 = 4

Time-Varying Channels Scenario:

Same delay bounds for all clients, τ≡τn Time-varying channels, pn(t) Applicable to Gilbert-Elliot fading Model

Joint Debt-Channel Policy: Let rn(t) be delivery debt Clients with larger rn(t) pn(t) get higher priorities

Theorem: The Joint Debt-Channel policy is feasibility optimal

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Heterogeneous Delay Bounds Scenario:

Static channels, pn≡pn(t) Different delay bounds for all clients, τn

Adaptive-Allocation Policy: Let rn(t) be time debt Estimate the # of slots needed by client n for a successful

transmission, ηn Dynamically allocate slots to maximize

( )nn

nr t

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Evaluation Methodology Evaluate four policies:

Proposed policies for each scenario PCF with randomly assigned priorities (random) Two policies proposed by [Hou, Borkar, and Kumar]

Time debt first policy Weighted-delivery debt first policy

Metric: Total delivery debt

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Rate Adaptation: VoIP Setup Period length = 20 ms Two groups of clients:

66 Group A clients and 44 Group B clients

Group A Group B

One packet every 60 ms One packet every 40 ms

21.3 kb/s traffic 32 kb/s traffic

require 19.2 kb/s timely throughput


Starting times evenly spaced

Data rates alternate between 11 Mb/s and 5.5 Mb/s

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Rate Adaptation: VoIP Results

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Time-Varying Channels: VoIP Setup Period length = 20 ms Two groups of clients:


Group A Group B






Channel evolves based on Gilbert-Elliot model

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Time-Varying Channels: VoIP Result

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Heterogeneous Delay Bounds: VoIP Setup Two groups of clients:


Group A Group B





Delay bound = 20 ms Delay bound = 13 ms


Average channel reliabilities between 80% and 96%

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Heterogeneous Delay Bounds: VoIP Result

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Conclusion Extend previous model for more complicated

scenarios With or without rate adaptation Time-varying channels Heterogeneous delay bounds

Identify a sufficient condition for optimal scheduling policies

Design policies for several cases Time-varying channels, heterogeneous delay bounds with

rate adaptation Time-varying channels without rate adaptation Heterogeneous delay bounds without rate adaptation

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Rate Adaptation: MPEG Setup Period length = 6 ms Two groups of clients:


Group A Group B

1700 kb/s traffic 1360 kb/s traffic

require 1530 kb/s timely throughput


Data rates alternate between 54 Mb/s and 24 Mb/s

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Rate Adaptation: MPEG Results

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Time-Varying Channels: MPEG Setup Period length = 6 ms Two groups of clients:


Group A Group B

1700 kb/s traffic 1360 kb/s traffic



Average channel reliabilities between 80% and 89%

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Time-Varying Channels: MPEG Setup

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Scheduling Heterogeneous Real- Time Traffic over Fading Wireless Channels I-Hong Hou P.R. Kumar...

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