1. Khiem Lam Jimmy Vuong Andrew Yang
A Unified Architecture for the Design and Evaluation of Wireless Fair Queueing Algorithms
Khiem Lam
Jimmy Vuong
Andrew Yang
ECE4605 – Fall 2007

2. Outline Present wireless fair service model
Present unified wireless fair queuing architecture
Evaluate and compare these 7 algorithms

3. Problems Location-dependent errors Bursty errors Channel contention
Joint scheduling of uplink and downlink flows

4. Example
Assume you have a packet cellular network with each base station performing the scheduling of the uplink and downlink transmissions, where communication can be only downlink or only uplink. Multiple channels are used and every mobile host can communicate with the base station.

5. Example Key characteristics of this wireless channel:
Wireless channel capacity is dynamically varying
Channel errors are location-dependent and bursty in nature
There is contention in the channel among multiple mobile hosts
Mobile hosts do not have global channel state
Scheduling deals with both uplink and downlink flows
Mobile hosts are often constrained in terms of processing power and battery power

6. Properties of Fluid Fair Queuing
Fairness among backlogged flows
Bounded delay channel access
Guaranteed minimum throughput for backlogged flows
In order to keep these properties, wireless channels require some properties:
Basically, fluid fair queuing provides a separation between flows so each flows don’t affect each other. Ex minimum flow guarantees are unaffected by the behavior of other flows. Fluid fair queuing assumes that the channel is error free or at least not location dependent. However, when there is location dependent errors, then it can’t provide fair or minimum throughput bounds.

7. Fair Queuing in Wireless Channels
Properties:
Short-term fairness
Long-term fairness
Channel-conditioned delay bounds
Short-term throughput bounds
Long-term throughput bounds
Delay sensitive and error sensitive data flows
Optimization of schedulable region

8. Issues in Wireless Fair Queuing
There are three major issues that need to be addressed:
The failure of traditional fluid fair queuing in the presence of location-dependent channel error
The compensation model for flows that preceive channel error: how transparent should wireless channel errors be to the user?
The trade off between full separation and compensation, and its impact on fairness of channel access

9. Five Components of Wireless Fair Queuing Architecture
Error-free service
Lead and lag model
Compensation model
Slot queues and packet queues
Channel monitoring and prediction
Goal is to emulate fluid fair queuing when channel is error free and when there are location dependent errors, swap clean channels with location error channels and vice versa.
Error-free service: Defines ideal fair service model assuming no channel errors
Lead and lag model: Determines which flows are leading/logging their error free service and by how much
Compensation model: compensates lagging flows that perceive an error-free channel at the expensive of leading flows, so it solves bursty and location dependent channel error
Slot queues and packet queues: allows support of delay sensitive and error sensitive flows; also decouples connection-level packet management policies from link level packet scheduling policies
Channel monitoring and prediction: provides reliable and accurate measurement and estimation of the channel state at any time instant for each backlogged flow

10. Error-free Service Model
Reference for how much service a flow should ideally receive
Packetized approximation of fluid fair queuing
Lead and Lag Model
Amount of service that can be compensated
Leadering, lagging, and in sync flows may change dynamically over time
Lag of a lagging flow = amount of additional service to which it is entitled in the future to compensate for lost service in the past
Lead of a leading flow amount of additional service that the flow can give up to compensative for the additional service it received in the past. 2 ways to compute lag and lead (see end of 3.2)

11. Compensation Model
Purpose is to reclaim the lost service due to channel error
Leading flows give up their excess service gradually
4 Situations:
No compensation
Flow with maximum lag
Leading and lagging flows swap slots
Bandwidth reserved for compensation

12. 7 Wireless Fair Queuing Algorithms
Channel State Dependent Packet Scheduling (CSDPS)
Idealized Wireless Fair Queueing (IWFQ)
Wireless Packet Scheduling (WPS)
Chanel-condition Independent Fair Queueing (CIF-Q)
Enhanced Class Based Queueing with CSDPS (CBQ- CSDPS)
Server Based Fairness Approach (SBFA)
Wireless Fair Service Algorithm (WFS)

13. Channel State Dependent Packet Scheduling (CSDPS)
Error-free Service: WRR Lead/Lag: Handles only error-free flows Compensation: No measure of lead/lag Complexity: O(n)
Simple
Disregards flows that perceives error, handles only error-free service
O(n) – checks if each flow is backlogged and perceives a clean channel

14. Idealized Wireless Fair Queuing (IWPQ)
Error-free Service: WFQ
Lead/Lag: Calculate lead/lag by comparing service tag to error-free service tag
Compensation: Lowest service tag gets priority to transmit
Complexity: O(nlogn)
Service tag is set to the finish tag of its head-of-line packet
O(nlogn) – sorting service tags

15. Wireless Packet Scheduling (WPS)
Error-free Service: WRR with WFQ
Lead/Lag: Increment/decrement lag of flows that are swapped.
Compensation: Intra-frame swapping and generating new frames base on “effective weight” flows
Complexity: O(n)
Intra-frame swapping – compensate flows that encounter channel error by locally trading slot allocations.
Lead/lag accounting mechanism – provide additional services tot the lagging flows at the expense of the leading flows by

16. Channel-condition Independent Fair Queuing (CIF-Q)
Error-free Service: Start Time Fair Queuing
Lead/Lag: Calculate lead/lag by comparing service tag to error-free service tag
Compensation: Leading flow relinquishes slots for lagging flows
Complexity: O(nlogn)
STFQ – similar to WFQ
Relinquished Slots – distributed among lagging flows in proportion to the lagging flows’ rate weights.
O(nlogn) – to sort service tags

17. Enhanced Class Based Queuing with CSDPS (CBQ-CSDPS)
Error-free Service: Modified CBQ with CSDPS
Lead/Lag: Based on actual number of bytes transmitted during each time window
Compensation Model: Lagging flows are given explicit precedence.
Complexity: O(n)
Lead/Lag: compared to normalized weight. Leading excess compared to normal. Lagging less than normal.
O(n) – same as CSDPS, to check if each flow is backlogged and perceives a clean channel

18. Server Based Fairness Approach (SBFA)
Error-free Service: Generic framework for adapting different service disciplines
Lead/Lag: Lag is measured by the number of slots in compensation flow (no measure for leading flows)
Compensation Model: Compensation flow treated like any other flow
Complexity: Dependent on error-free service
Sensitive to statically reserved fraction

19. Wireless Fair Service (WFS)
Error-free service: Enhanced WFQ to support delay- bandwidth decoupling
Lead/Lag: Lead is the # of slots it gives up in the future whereas lag is the # of slots the flow is entitled to make up in the future.
Compensation Model: Leading flow with lead, l, and lead bound of lmax relinquishes l/lmax slots allocated to it
Complexity: O(nlogn)
O(nlogn) – to sort service tags

20. Wireless Fair Queuing Algorithms
Summary
/ - coarsely achieved
*1 – only for error-free channels
Wireless Fair Queuing Algorithms
Wireless Fair Service Model Properties
CSDPS
IWFQ
WPS
CIF-Q
CBQ-CSDPS
SBFA
WFS
Short-term Fairness / ✔
Long-term Fairness
Channel-conditioned Delay Bounds
Short-term Throughput Bounds
Long-term Throughput Bounds
✔*1
Optimization of the Schedulable Region

21. Simulation Results Average result over 40 simulation runs
CBR, Poisson, and MMPP sources
Markov chain wireless channel
Six Examples

22. Examples 1 & 2
Example 1: Error-free Service – All algorithms perform to their error-free models
Example 2: Error-sensitive vs. Delay-sensitive flows

23. Example 3 – Service Degradation

24. Example 4, 5, & 6 Example 4: Channel Prediction
Worse channel prediction, worse performance
Example 5: Identical Behavior
Moderately loaded network
Moderate error patterns
Large number of sources
Example 6: Adaptive Sources
Effect of latency adaptation on throughput in channel error

25. Related Work Adaptation for wireless LAN, WAN, PAN, etc.
Adaptive sources (http, streaming)
Forward Error Correction

26. Conclusion and Critique
First work that provides evaluation of different wireless fair queuing algorithms
WFS and CIF-Q achieve all properties of wireless fair service
Paper needed more revising (typos, etc.)
Paper did not give many details on algorithms