Optimization of Scheduling Algorithm Parameters in a DiffServ Environment

1. Optimization of Scheduling Algorithm Parameters in a DiffServ Environment Authors: Davide Adami Stefano Giordano Michele Pagano Raffaello Secchi Speaker: RaffaelloSecchi Network Telecommunication Group University of Pisa - Information Engineering departmentJanuary 31 2005

2. Outline • Introduction to scheduling algorithms • Deficit Weighted Round Robin • Weighted Fair Queuing • Objective of our study • Performance Comparison between DRR and WFQ scheduler • Derivation of a configuration strategy of scheduling parameters to minimize the end-to-end delay of real-time application in DRR networks • Numerical Analysis • Simulation results in high speed networks • Conclusions

3. Scheduling Algorithms W1 The weight associated to the i-th queue is proportional to the percentage of output capacity Server W2 s Output link W3 • In this work we considered two different proportional schemes • Deficit Round Robin (frame-based scheduler) • Weighted Fair Queuing (sorted priority scheduler) WFQ • It schedules packets emulating the behavior of an ideal fluid system (GPS) • High performance in terms of end-to-end and delay jitter • It provides a fair distribution of service and a good isolation between flows • Logarithmic complexity with respect to the number of flow DRR • It visits, in a round robin fashion, all non-empty traffic queues: at each turn it sends a mean amount of data of the flow (quantum) • It may introduce a higher latency than WFQ • Computational complexity independent from the number of queues Our goal is to configure DRR weights in order to approximate the performance of WFQ system in terms of end-to-end delay and delay jitter

4. Expedited Forwarding sources EF traffic collectors Scheduler Assured Forwarding sources Backbone Link Primary Path 100Mbps Links BE traffic collectors Best Effort traffic 1Gbps Link Reference DiffServ Network Scenario AF traffic collectors • We consider a simple DiffServ Model with only three classes (EF, AF e Best Effort) • The EF class deliver packets for real-time and delay sensitive applications • The AF class carries traffic for applications with less stringent timing requirements than EF: AF packets should be delivered within a predefined time interval with low losses. • The Best Effort applications tolerate with highly variable transmission delay and delay variation

5. Traffic characterization with Token Bucket Model In this study we characterize the AF and EF traffic aggregated flows through a token bucket model: token rate Mean bitrate of EF aggregated traffic Token buffer token depth EF class burstiness. Maximum deviation from mean long term behavior s EF traffic aggregate output link Bound on amount of EF traffic injected into the network during the interval (t0,t]

6. Latency-Rate scheduler model The LR scheduler model is based on the concept of latency and mean guaranteed rate: • The latency is the time needed to the LR-scheduler to provide the mean guaranteed rate to the i-th flow • The Deficit Weighted round robin scheduler is a LR-scheduler, whose latency is expressed by the following expression: where EF session weight maximum packet size for active sessions number of sessions EF class quantum output link capacity

7. Bound on EF class end-to-end delay The worst-case delay of EF class packets in a network made of a cascade of k LR-scheduler is given by: Latency of j-th scheduler for EF class Minimum guaranteed rate for EF class Burst-size of token-bucket model for EF class. We evaluate the IPDT bound of AF and EF class for the reference DiffServ network scenario considering the delay constraints Then, normalizing the weight through AF=wAF/wBEand BE=wEF/wBE , we obtain a function expressing the EF and AF classes worst-case delay as a function of TB parameters and quantum

8. Choice of working parameters The previous analysis has determined the parameters characterizing the delay bound. In order to select a configuration of weights we can exploit the degree of freedom • The ratio AFbetween AF and BE class quantum is obtained by enforcing a maximum delay on AF class packets • By choosing EF on the knee-point of token-bucket curve EF(EFmin), we can have a tradeoff between the maximum EF class delay and bandwidth requirements In order to evaluate the impact BE quantum on DRR and WFQ performance we study the behavior of scheduling system in a limited range of values, observing just small variations

9. DRR-bnd 240Kb DRR-bnd 120Kb DRR-bnd 60Kb WFQ-bnd Strategy of DRR Weight Configuration End-to-end delay bound comparison for EF class DWRR and WFQ by varying the BE quantum The minimum is obtained by deriving maximum delay function Applying this condition to weights associated to DRR to EF, AF e BE service classes means: Analytically: The minimization of worst-case delay IPTD EF class Experimentally: the minimization of performance gap between DWRR and WFQ in terms of maximum delay and delay variation

10. Expedited Forwarding sources EF traffic collectors Scheduler Assured Forwarding sources Backbone Link Primary Path 100Mbps Links BE traffic collectors Best Effort traffic 1Gbps Link Simulation Setup NS-2 simulation topology Performance Metrics • IP Transfer Delay (IPTD): end-to-end delay experienced by i-th packet • IP Delay Variation (IPDV): end-to-end delay variation experienced by packet with respect to a reference delay • We evaluate the mean of maximum IPTD and mean IPDV in a set offive simulations of about 60sec for each BE value .

11. DRR-bnd WFQ-bnd DRR-sim WFQ-sim DRR-bnd WFQ-bnd DRR-sim WFQ-sim Simulation Results (maximum IPTD) Maximum IPTD comparison for EF class (QBE =7.5KB and QBE =30KB) The worst-case bound is very conservative with respect to results of simulations but the behavior of both curve is very similar By assigning to DWRR classes the BE obtained through previous analysis, we can observe … The minimization of worst-case IPDT for EF class packets The reduction of loosing of performance between DWRR and WFQ schedulers

12. DRR-sim WFQ-sim DRR-sim WFQ-sim Simulation Results (average IPDV) Average IPDV comparisons for EF class between DWRR and WFQ (QBE =7.5KB and 30KB) Larger the BE Quantum larger the size of DRR frame for a single round-robin service cycle For a large DWRR frame, the inter-departure time of packets delivered in consecutive rounds may be considerable. Then, it is necessary to avoid the use of too large BE quantum

13. Second set of simulations We incremented the AF class load in terms of mean bitrate and burstiness, while keeping the same traffic in EF and BE classes The AF traffic aggregate flow was obtained by multiplexing of sixty VIC flows

14. DRR-sim test 2 WFQ-sim test 2 DRR-sim test 1 WFQ-sim test 1 Test results comparisons (worst-case IPTD) Maximum IPTD comparison for EF class between first and second test As we could expect, the worst-case IPDT increasing is larger in the case of DWRR scheduler than WFQ scheduler. Since the WFQ scheduler behavior is close to ideal GPS system, it guarantees a quite perfect flow isolation However, for the selected configuration of weights, we reach again the minimization of DWRR end-to-end transmission delay and the reduction of performance gap with respect to WFQ

15. Conclusions • This work has led to the definition of an optimization strategy to configure the bandwidth allocated to different DiffServ flows • Simulation results validate the effectiveness of technique in selecting the best DWRR operating point • This procedure allows the minimization of worst-case IPDT of privileged class, while limiting the delay of other classes to prearranged thresold • Moreover, this strategy allow to reduce the differnce in performance between DRR and WFQ schedulers in terms both of IPDT and IPDV