Campus Units

Electrical and Computer Engineering, Mathematics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2-2018

Journal or Book Title

ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS)

Volume

3

Issue

1

First Page

3

DOI

10.1145/3159172

Abstract

We study cloud storage systems with a very large number of files stored in a very large number of servers. In such systems, files are either replicated or coded to ensure reliability, i.e., to guarantee file recovery from server failures. This redundancy in storage can further be exploited to improve system performance (mean file-access delay) through appropriate load-balancing (routing) schemes. However, it is unclear whether coding or replication is better from a system performance perspective since the corresponding queueing analysis of such systems is, in general, quite difficult except for the trivial case when the system load asymptotically tends to zero. Here, we study the more difficult case where the system load is not asymptotically zero. Using the fact that the system size is large, we obtain a mean-field limit for the steady-state distribution of the number of file access requests waiting at each server. We then use the mean-field limit to show that, for a given storage capacity per file, coding strictly outperforms replication at all traffic loads while improving reliability. Further, the factor by which the performance improves in the heavy traffic is at least as large as in the light-traffic case. Finally, we validate these results through extensive simulations.

Comments

Copyright ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Li, Bin, Aditya Ramamoorthy, and R. Srikant. "Mean-Field Analysis of Coding Versus Replication in Large Data Storage Systems." ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS) 3, no. 1 (2018): 3. DOI: 10.1145/3159172.

Copyright Owner

ACM

Language

en

File Format

application/pdf

Published Version

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