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

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

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

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.

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

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.

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

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

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

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)

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

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)

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

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

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

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

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

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

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

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

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

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

Example 3 – Service Degradation

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

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

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