Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Optimization of Scheduling Algorithm Parameters in a DiffServ Environment Authors: Davide Adami Stefano Giordano Michele Pagano Raffaello Secchi Speaker: Raffaello Secchi Network Telecommunication Group University of Pisa - Information Engineering department January 31 2005 1 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 2 Scheduling Algorithms The weight associated to the i-th queue is proportional to the percentage of output capacity W1 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 DRR •It schedules packets emulating the behavior of an ideal fluid system (GPS) •High performance in terms of end-toend 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 •It visits, in a round robin fashion, all nonempty 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 3 Reference DiffServ Network Scenario Expedited Forwarding sources Assured Forwarding sources EF traffic collectors Scheduler Backbone Link AF traffic collectors Primary Path 100Mbps Links Best Effort traffic 1Gbps Link • 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 4 BE traffic collectors Traffic characterization with Token Bucket Model In this study we characterize the AF and EF traffic aggregated flows through a token bucket model: ~EF token rate EF Token buffer ~EF token depth EF s EF traffic aggregate EF class burstiness. Maximum deviation from mean long term behavior output link AEF (t0 , t ) ~EF (t t0 ) EF 5 Mean bitrate of EF aggregated traffic Bound on amount of EF traffic injected into the network during the interval (t0,t] 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: EF (n 1 DRR j EF wj Lmax ) wEF r j EF w j QEF wEF r where wEF 6 EF session weight n number of sessions r output link capacity Lmax maximum packet size for active sessions QEF EF class quantum 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: Minimum guaranteed rate for EF class EF EF EF min Dmax EF min j EF Latency of j-th scheduler for EF class k j j ( EF ) EF j 1 EF Burst-size of tokenbucket 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/wBE and BE=wEF/wBE , we obtain a function expressing the EF and AF classes worst-case delay as a function of TB parameters and quantum EF Dmax ( AF , BE , EF , QBE ) ( 7 1 BE NL NQBE 2 NLmax EF )( EF max ) (1 BE ) AF r r AF r r r 1 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 EF Dmax ( AF , BE , EF , QBE ) •The ratio AF between 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 AEF (t0 , t ) EF min (t t0 ) EF EF 300KB EF 10.3MB 8 Strategy of DRR Weight Configuration DRR-bnd 240Kb DRR-bnd 120Kb DRR-bnd 60Kb 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 EF Dmax ( AF , BE , EF , QBE ) NLmax NQBE r EF 0 2 BE BE AF WFQ-bnd BE AF ( EF NLmax ) NQBE 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 9 Simulation Setup NS-2 simulation topology Expedited Forwarding sources Assured Forwarding sources EF traffic collectors Scheduler Backbone Link Primary Path 100Mbps Links Best Effort traffic 1Gbps Link BE traffic collectors 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 of five simulations of about 60sec for each BE value 10 . 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 QBE 7.5KB BE 7.38 DRR-bnd WFQ-bnd DRR-sim WFQ-sim QBE 30 KB DRR-bnd WFQ-bnd DRR-sim WFQ-sim BE 4.47 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 11 Simulation Results (average IPDV) Average IPDV comparisons for EF class between DWRR and WFQ (QBE =7.5KB and 30KB) QBE 7.5KB BE 7.38 DRR-sim WFQ-sim QBE 30 KB DRR-sim WFQ-sim BE 4.47 •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 12 Second set of simulations Aggregated traffic flow First simulation second simulation average bitrate 71.92 Mbps 129 Mbps peak-rate 0.1sec interval 98.2 Mbps 308 Mbps • 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 13 Test results comparisons (worst-case IPTD) Maximum IPTD comparison for EF class between first and second test DRR-sim test 1 WFQ-sim test 1 DRR-sim test 2 WFQ-sim test 2 QBE 30 KB QBE 30 KB BE 4.38 • 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 14 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 15