Download presentation

Presentation is loading. Please wait.

Published byRobert Alvarez Modified over 2 years ago

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

2
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
3 In this work we considered two different proportional schemes Deficit Round Robin ( frame-based scheduler) Weighted Fair Queuing ( sorted priority scheduler) Scheduling Algorithms 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 s Server Output link W1W1 W2W2 W3W3 The weight associated to the i-th queue is proportional to the percentage of output capacity 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

4
4 Reference DiffServ Network Scenario AF traffic collectors Primary Path Backbone Link 100Mbps Links 1Gbps Link Expedited Forwarding sources Assured Forwarding sources Best Effort traffic EF traffic collectors BE traffic collectors Scheduler 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
5 Traffic characterization with Token Bucket Model In this study we characterize the AF and EF traffic aggregated flows through a token bucket model: EF class burstiness. Maximum deviation from mean long term behavior Mean bitrate of EF aggregated traffic Bound on amount of EF traffic injected into the network during the interval (t 0,t] Token buffer s EF traffic aggregate token rate token depth output link

6
6 Latency-Rate scheduler model maximum packet size for active sessions EF class quantum EF session weight number of sessions output link capacity 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

7
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: Minimum guaranteed rate for EF class Latency of j-th scheduler 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 =w AF /w BE and BE =w EF /w BE, we obtain a function expressing the EF and AF classes worst-case delay as a function of TB parameters and quantum

8
8 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 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 Choice of working parameters 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
9 DRR-bnd 240Kb DRR-bnd 120Kb DRR-bnd 60Kb WFQ-bnd The minimum is obtained by deriving maximum delay function End-to-end delay bound comparison for EF class DWRR and WFQ by varying the BE quantum 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 Applying this condition to weights associated to DRR to EF, AF e BE service classes means: Strategy of DRR Weight Configuration

10
10 Simulation Setup. Primary Path Backbone Link 100Mbps Links 1Gbps Link Expedited Forwarding sources Assured Forwarding sources Best Effort traffic EF traffic collectors BE traffic collectors Scheduler 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 NS-2 simulation topology

11
11 DRR-bnd WFQ-bnd DRR-sim WFQ-sim DRR-bnd WFQ-bnd DRR-sim WFQ-sim 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 Maximum IPTD comparison for EF class (Q BE =7.5KB and Q BE =30KB) The worst-case bound is very conservative with respect to results of simulations but the behavior of both curve is very similar Simulation Results (maximum IPTD)

12
12 Simulation Results (average IPDV) Average IPDV comparisons for EF class between DWRR and WFQ (QBE =7.5KB and 30KB) DRR-sim WFQ-sim DRR-sim WFQ-sim 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
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 Aggregated traffic flow First simulation second simulation average bitrate Mbps129 Mbps peak-rate 0.1sec interval 98.2 Mbps308 Mbps

14
14 Test results comparisons (worst-case IPTD) Maximum IPTD comparison for EF class between first and second test DRR-sim test 2 WFQ-sim test 2 DRR-sim test 1 WFQ-sim test 1 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
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

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google