integrated qos for wireless packet networks

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Center for Telecommunications ResearchCOMET Group, Columbia University comet.columbia.edu/wireless

Integrated QOS for WirelessPacket Networks

Javier Gomez and Andrew T. CampbellDepartment Of Electrical Engineering

Columbia University

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Problem

Channel impairments time scale

packet

flow/session time

modulation FEC Interleaving?

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Is this necessary?

Slow/fast fading

out of range

low power

interference

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Problems

• Flow/session adaptation mechanisms

Channel impairmentstime scale

packet flow/session

Time scale

modulation FEC Interleaving prediction compensation Adaptation

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Channel Prediction

• Advantage: Efficient use of bandwidth.

• Problem: Is it possible to predict the state of the channel?

– Previous work

• The state of the channel is known• The channel remains in bad state for 200 milliseconds

• The predictor is 100 percent accurate

• factors in channel prediction:• behavior of the channel• prediction strategy• packet size

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Predictor

CTS

RTS

ACK

DATA

Base Station Mobile host

Transmission

Prediction

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Complex interactions...

rts cts data ack

Examples :

• packet

Bad channel

Good channel

time

•Wireless channel is modeledas a two state Markov process•Is this a valid model ?• Can the results using thismodel be generalized?

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Prediction accuracy

• Channel• good 10:1 bad

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Without prediction

• Flow test: 500 kbps

• Channel :

– good: 20,000 bytes

– bad: 8,000 bytes

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With prediction

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Compensator

• Problem: Prediction does not compensate mobiles that deferredtransmission

• Solution: The scheduler must compensate mobiles deferringtransmission

• Previous work

• Assumed perfect channel prediction

• complex solutions, WRR• Try to minimize the latency bounds

• Our solution

• Simple, DRR• minimize latency bound is difficult

– The channel is beyond control

– The MAC (IEEE 802.11) makes latency worst

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WRR

• Quantum size (QS): Weight of each session• Deficit counter (DC): Credit record of each session

200250 100

200 100

350100

100

100 100

100

300 200

DC QS

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Compensator

• Quantum size (QS): Weight of each session

• Deficit counter (DC): Credit record of each session• Compensation Counter (CC): maintains the compensation credit

200250 100

150 100

250100

250

100 100

150

300 100

DC QS

0

400

200

CC

100

200

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Compensator

• Problem 1: How much credit should a flow receive after transmissionwas deferred?

• Solution: Compensate according to the load– low credit when little load– a quantum size for heavy load

200250 100 400 150

DC QS

400

CC

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Compensator

• Problem 2: How much compensation in one round?

• Solution:

• Use available bandwidth if possible

• Bound maximum compensation if system is heavy loaded

200250 100

150 100

250100

250

100 100

150

300 100

DC QS

0

400

200

CC

100

200

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Compensator

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Compensator

• Fairness:

• The compensator achieves fairness unless:• Channel prediction fails• Good state periods are not long

enough• Buffer overflows and packets are

dropped

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Bounds...

• Channel: Good 10:1 Bad

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Adaptation

• Do selecting dropping.

• Applications can transmit with different levels of hierarchy– Examples

• MPEG-1 (I frame, P frame, B frame)• MPEG-2 (Base layer, Enhancement layers)• MPEG-4 (multiple objects)

• When the buffer is about to overflow, drop low priority packetsfirst

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Adaptation

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Drop mark...

• Tradeoff:

• If the average buffer occupancy is large, then set thedrop mark near the head of the buffer

• if the average buffer occupancy is small, then set thedrop mark near the head of the buffer

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Conclusions/Future Work

• Channel prediction, compensation and adaptation need towork in unison.

• We have shown the limitations of:– Channel prediction– Compensation– flow adaptation

• Implement this framework in a testbed– New 802.11 radios/freeBSD

integrated qos for wireless packet networks

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