1. Nathan Daniel Anil Koneri Vineeth Chander Yuhang Lin Jaime Johnson
Unified Architecture for Design and Evaluation of Wireless Fair Queueing Algorithms
Nathan Daniel Anil Koneri
Vineeth Chander
Yuhang Lin
Jaime Johnson

2. Introduction Wireless fair queuing(WFQ)
Present a wireless fair service model
Present a unified wireless fair queueing architecture
Fair queuing algorithms (CSDPS, IWFQ, WFS etc)
Comparison of algorithms based on short/ long term fairness, throughput and delay bounds

3. Background and motivation
Growing use of wireless networks
Need for short/long term fairness as wireless channel is a critical scarce resource
Characterize a desired service model in terms of a Wireless fair service
Achieve wireless fair service with a unified WFQ architecture
Provide single framework to compare different WFQ algorithms
Serve as a framework to develop new wireless scheduling algorithms

4. Problem statement
Adapting wireline fair queueing algorithms to wireless domain is hard due to problems in wireless channels
Location dependent errors
Channel contention etc.
Fluid fair queuing neither fair nor able to provide minimum throughput bounds

5. Wireless Fair Service Model
Short/long term fairness among backlogged flows that perceive a clean channel/ bounded channel error
Channel conditioned delay bounds for packets
Short/ long term throughput bounds for flows with clean channels/ bounded channel error
Support for delay sensitive and error sensitive data flows
Optimization of schedulable region by decoupling delay and bandwidth requirements

6. Issues in WFQ
Failure of fluid fair queueing in presence of location dependent channel error
Compensation model for flows that perceive channel error
Trade off between full separation and compensation
Due to location dependant errors, service algorithms meant to be fair become inconsistent over different time intervals
Unaddressed issues include:
Uplink flow state by base station
Channel state prediction inaccuracies
Scheduling and medium access coordination

7. Unified WFQ framework Error free service-serve as reference
Lead/lag model
Lag/lead = (error free service)-(real service)
Lag:# of slots assigned to backlogged flow during which it cannot transmit but another flow could
Compensation model
No explicit compensation
Preference given to maximum lag flow
Leading and lagging flows swap slots
Bandwidth reservation
Slot queues and packet queues
Channel monitoring and prediction

8. Channel State Dependent Packet Scheduling
Allows for the use of any Error-Free Service.
In terms of Lead/Lag model, has no measure of leading/lagging flows
No long/short term fairness or compensation
Low implementation complexity: O(N)

9. Idealized Wireless Fair Queuing
Error-Free service functions by sending packet with minimum finish tag first
Lag classified as undesired difference between present flow and flow in error-free service, with a lead being the opposite.
Compensation model favors channel access for lagging flows
Implementation complexity: O(NlogN)

10. Wireless Packet Scheduling
Error-Free service functions by slotting frames of various sizes to be sent
Lag is compensated by swapping slots in the frame when an erroneous channel is sensed, with frames being generated based on effective flow weights
Compensation model mainly consists of inter-frame swapping, with in sync flows being unaffected.
Implementation complexity: O(N)

11. Channel-Condition Independent Fair Queuing
Error-Free service functions similarly to Wireless Fair Queuing algorithm
Leading flows relinquishing a slot with a probability of , a system parameter to be allocated to flow with maximum lag
Compensation model allows lagging flows to receive additional service when a leading flow relinquishes a slot
Implementation complexity: O(N)

12. Enhanced Class Based Queuing with Channel State Dependent Packet Scheduling
Error-free service is a combination of class based queuing and channel state packet scheduling
CBQ-CSDPS maintains flow lead/lag based on the number of bytes transmitted and gives transmission precedence to lagging flows to make up lag
Compensation model consists of giving channel access precedence to lagging flows
Implementation complexity: O(N)

13. Server-Based Fairness Approach
Error-free service consists of adapting difference service disciplines to the wireless domain
SBFA reserves a certain fraction of the channel bandwidth for compensation by specifying a virtual compensation flow
Compensation model consists of compensation flow which is treated as any other flow and shared by all lagging flows
Implementation complexity: Dependent on choice of error-free service

14. Wireless Fair Service Algorithm
Error-free service consists of an enhanced version of WFQ
Flows perceiving an error channel has their lag increased by another channel being given their slot to transmit
Compensation model consists of a leading flow with a lead of “L” releasing a fraction of L/Lmax of the slots allocated to it to be fairly distributed to lagging flows
Implementation complexity: O(N) / O(NlogN)

15. Performance Evaluation
Evaluate the performance of each algorithm by considering:
Seperation between flows
Decupling of rate and delay.
Size of schedulable region.
Short and long term throughput and fairness guarantees.
Graceful service degradation for leading flows.
Simulations had a typical run of 50k time units.
Average each result over 40 simulation runs.
Measure parameter over 10 different time windows, size 200 time units each, in a single simulation run, average the values obtained over 5 distinct simulation runs.

16. Error-free Service Results
Each algorithm performs according to its error-free service model.
Rates obtained by flows are proportional to their weight.

17. Error-free Service Results
WSF simulation
Delay weights
Flow 1 = 0.9, Flow 2 = 0.09, Flow 3 = 0.009
(I) Simulation over entire run, (II) Simulation over small time window
Source 1 has a larger delay weight, experiences a much smaller delay.
Source 3 has a smaller delay weight, experiences a large delay.

18. Error-sensitive vs. Delay-sensitive
Delay-sensitive flow drops packet when packets are in queue for a time larger than specified delay bound
Error-sensitive flow drops packets when it tries to transmit a packet for a fixed number of times and encounters error on all attempts.
Source 1 and 2 are MMPPs with ON rate of 1.5, steady state probability PG=0.7
Source 3 is a constant source with rate of 0.25, and has an error-free channel.
Flow 1 retransmission bound is 8.
Flow 2 and 3 delay bound is 100.

19. Error-sensitive vs. Delay-sensitive Throughput
Flow 1 and 2 get equal throughput in all algorithms.
CSDPS perform as well as WPS.
Flow 3 gets its due rate even though the other flows are in error.
Error-free flows achieve their long term throughput guarantees under all algorithms.

20. Error-sensitive vs. Delay-sensitive Loss rates and delay
IWFQ: Flow 2 Loss rate and Flow 1 packet delays are much less than other algorithms. Very high delay for error-free Flow 3.
SBFA: High delay and packet loss rates for flows with channel error (Flow 1 and 2).

21. Channel Prediction
Similar to Error-sensitive vs. Delay-sensitive example except the channel errors now are uncorrelated between slots.
SBFA: performs well even when channel prediction is poor.
Worse the channel prediction, worser the performance.

22. Identical Behavior
Assume all wireless fair queuing algorithms behave in a similar way.
6 Sources all have identical error patterns.
Modeled as Markov Chain, pg+pe=0.01, PG=0.7
All sources are MMPP with average rate = 0.04
Delay Bound = 150.
All delays are similar except SBFA.
As number of flows increases, all flows have same error patterns and offered traffic is stable but moderately heavy. Compensation algorithms start to work approximately the same.

23. Related Work
“Fair Scheduling in wireless packet networks”- S.Lu, V.Bharghavan, R.Srikant (slot/ packet queues & channel prediction, WFS etc.)
“A unified Architecture for WFQ Algorithms: Analysis and Evaluation”- S.Lu, V.Bharghavan, T. Nandagopal (detailed description of analysis of performance & results )
“Packet Fair Queueing Algorithms for Wireless Networks with location depedent errors”- T.S. Ng, I.stoica, H.Zhang (CIF-Q)

24. Critique
Flow specific parameters ei, Ti for delay bound requirement of service model is assumed, no logic or rationale provided.
Rate weight ri is allocated…?
Leading/lagging flows swap slots – lead flows give up a constant, variable fraction of slots, no values or what its based ?
In IWFQ, B - scheduler parameter, provides no information as to what it is, how its set etc.
In CIF-Q, α – system parameter, no background information, what values etc.
CBQ-CSDPS , properties are sensitive to time window of measurement - how and what variations
WFS, delay weight Φi – how is it computed, what does it depend on ?
Test for variation in average rates & other parameters.

25. Conclusions
Wireless fair service model captures requirements of present scheduling algorithms.
WFQ architecture serves as a framework for future design of algorithms.
The model helps comparing different WFQ algorithms and evaluate trade-offs between them.
WFS and CIF-Q achieves all properties and hence are preferred.

26. Questions…?