islip throuput fairness tradeoffs

Topics: Output, Round-robin scheduling, Input Pages: 9 (5675 words) Published: April 27, 2014
Throughput/Fairness Trade offs for the iSLIP Scheduling
Algorithm
Petros Mol

Todor Ristov

Nikolaos Trogkanis

University of California, San
Diego

University of California, San
Diego

University of California, San
Diego

pmol@cs.ucsd.edu

tristov@cs.ucsd.edu

nikos@cs.ucsd.edu

ABSTRACT
High throughput and fairness consist two desirable properties when scheduling traffic in an Input-Queued crossbar switch. Unfortunately, these two goals are conflicting which makes the job of most scheduling algorithms that want to

achieve both hard. Here, we investigate the trade offs between throughput and fairness for iSLIP, one of the most well-studied algorithms introduced in [4]. We study iSLIP’s behavior under several traffic patterns both for persistent

and for Bernoulli arrivals and compare its throughput to
the throughput achieved by a maximum matching algorithm
which is less efficient and completely ignores fairness. We
conclude that iSLIP’s superiority in fairness comes with only a minor degradation of throughput. Hence iSLIP seems to
achieve a great balance between performance, throughput
and fairness.

1.

INTRODUCTION

Input-Queued (IQ) switches are massively used in network
design. The central problem in designing an input-queued
switch is the scheduling algorithm that decides which packets should be transfered from input ports to output ports in a given time slot. Iterative algorithms consist a very important class of IQ switches’ scheduling algorithms because they can achieve very good performance due to pipelining.

The main properties desired by a good scheduling algorithm are high throughput, starvation-freeness, speed and simplicity. In this project we study one of the most celebrated iterative scheduling algorithms, iSLIP, and try to investigate how iSLIP balances these properties. Our goal

is to analyze its performance in several different scenarios and get a deeper understanding of its characteristics.
The central question in this work is how iSLIP manages to
balance the requirements for high fairness and high throughput. We also study the following two questions: 1. How are throughput and/or fairness affected by the number of iterations per round? 2. How does iSLIP’s behavior change with different kinds of traffic (uniform, skewed etc). In particular, how sensitive is the throughput achieved by iSLIP in several traffic patterns?

We use the standard definition for measuring throughput as well as the Max-Min and Absolute fairness definition for measuring fairness. We also consider several different
traffic patterns for both persistent traffic and Bernoulli arrivals. We measure iSLIP’s throughput for various traffic and for variable load and compare it with the throughput

achieved by a maximum matching algorithm. The throughput achieved by iSLIP is very close to the one achieved by the maximum matching algorithm even though the latter incurs
a much higher overhead (in order to compute the maximum
matching) and completely ignores fairness.
Several traffic patterns are also tried for measuring fairness. We measure fairness in two ways: the Max-Min definition takes into account the specific traffic pattern whereas the absolute fairness definition only considers the ratio between the rates of the most served and the least served queue and is completely oblivious of the traffic. Our simulations show that under the Max-Min definition iSLIP performs fairly well for most of the traffic patterns. Under the absolute fairness definition, iSLIP seems to perform rather poorly for some patters. In all cases, however, there are no queues starved.

In all experiments it is clear that high throughput usually
comes at the cost of lower fairness and vice versa. Still,
iSLIP, even if its parameters (number of iterations per round and number of iteration after which no pointers are updated) are fixed regardless of the pattern, manages to combine well characteristics (high throughput and fairness).

Roadmap: We start by...


References: [1] D. Bertsekas and R. Gallager. Data Networks. Prentice
Hall, 2nd edition, 1992.
International Conference on Communications, 2002.
ICC 2002, volume 2, 2002.
Networking, 7(2):188–201, 1999.
devices. Morgan Kaufmann, 2005.
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