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Heavy Tails in Queueing Systems: Impact of Parallelism on Tail Performance

Part of: Stochastic processes Computer system organization

Published online by Cambridge University Press:  30 January 2018

Bo Jiang ,
Jian Tan ,
Wei Wei ,
Ness Shroff  and
Don Towsley
Bo Jiang*
Affiliation:
University of Massachusetts
Jian Tan*
Affiliation:
The Ohio State University and IBM T. J. Watson Research
Wei Wei*
Affiliation:
University of Massachusetts
Ness Shroff*
Affiliation:
The Ohio State University
Don Towsley*
Affiliation:
University of Massachusetts
*
∗ Postal address: School of Computer Science, University of Massachusetts, 140 Governors Drive, Amherst, MA 01003, USA.
∗∗∗ Postal address: IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA. Email address: tanji@us.ibm.com
∗ Postal address: School of Computer Science, University of Massachusetts, 140 Governors Drive, Amherst, MA 01003, USA.
∗∗∗∗∗ Postal address: Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA. Email address: shroff@ece.osu.edu
∗ Postal address: School of Computer Science, University of Massachusetts, 140 Governors Drive, Amherst, MA 01003, USA.
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Abstract

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In this paper we quantify the efficiency of parallelism in systems that are prone to failures and exhibit power law processing delays. We characterize the performance of two prototype schemes of parallelism, redundant and split, in terms of both the power law exponent and exact asymptotics of the delay distribution tail. We also develop the optimal splitting scheme which ensures that split always outperforms redundant.

Keywords

Multipathpower lawparallelism

MSC classification

Primary: 68M20: Performance evaluation; queueing; scheduling
Secondary: 60G99: None of the above, but in this section
Type
Research Article
Information
Journal of Applied Probability , Volume 50 , Issue 1 , March 2013 , pp. 127 - 150
Copyright
Copyright © Applied Probability Trust 2013 

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