research-article

Using machine learning techniques to enhance the performance of an automatic backup and recovery system

Authors:

Eran Raichstein,

Amir RonenAuthors Info & Claims

SYSTOR '10: Proceedings of the 3rd Annual Haifa Experimental Systems Conference

Article No.: 1, Pages 1 - 10

https://doi.org/10.1145/1815695.1815697

Published: 24 May 2010 Publication History

Abstract

A typical disaster recovery system will have mirrored storage at a site that is geographically separate from the main operational site. In many cases, communication between the local site and the backup repository site is performed over a network which is inherently slow, such as a WAN, or is highly strained, for example due to a whole-site disaster recovery operation.

The goal of this work is to alleviate the performance impact of the network in such a scenario, and to do so using machine learning techniques. We focus on two main areas, prefetching and read-ahead size determination. In both cases we significantly improve the performance of the system.

Our main contributions are as follows: We introduce a theoretical model of the system and the problem we are trying to solve and bound the gain from prefetching techniques. We construct two frequent pattern mining algorithms and use them for prefetching. A framework for controlling and combining multiple prefetch algorithms is presented as well. These algorithms, as well as various simple prefetch algorithms, are compared on a simulation environment. We introduce a novel algorithm for determining the amount of read ahead on such a system that is based on intuition from online competitive analysis and on regression techniques. The significant positive impact of this algorithm is demonstrated on IBM's FastBack system.

Much of our improvements have been applied with little or no modification of the current implementation's internals. We therefore feel confident in stating that the techniques are general and are likely to have applications elsewhere.

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Cited By

Singh GSharma K(2022)The Performance Optimization for Save and Restore Applications of Switching to New Firmware Using a Clean Install Approach2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10059982(299-303)Online publication date: 18-Nov-2022
https://doi.org/10.1109/IIHC55949.2022.10059982
Song YRoutray RHou Y(2014)Scalable data analytics platform for enterprise backup management2014 IEEE Network Operations and Management Symposium (NOMS)10.1109/NOMS.2014.6838291(1-7)Online publication date: May-2014
https://doi.org/10.1109/NOMS.2014.6838291

Index Terms

Using machine learning techniques to enhance the performance of an automatic backup and recovery system

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Published In

cover image ACM Other conferences

SYSTOR '10: Proceedings of the 3rd Annual Haifa Experimental Systems Conference

May 2010

211 pages

ISBN:9781605589084

DOI:10.1145/1815695

Conference Chair:
Gadi Haber
IBM
,
Program Chairs:
Dilma M. Da Silva
IBM
,
Ethan L. Miller
UCSC

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 May 2010

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SYSTOR '10

SYSTOR '10: The 3rd Annual Haifa Experimental Systems Conference

May 24 - 26, 2010

Haifa, Israel

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Overall Acceptance Rate 108 of 323 submissions, 33%

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Cited By

Singh GSharma K(2022)The Performance Optimization for Save and Restore Applications of Switching to New Firmware Using a Clean Install Approach2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)10.1109/IIHC55949.2022.10059982(299-303)Online publication date: 18-Nov-2022
https://doi.org/10.1109/IIHC55949.2022.10059982
Song YRoutray RHou Y(2014)Scalable data analytics platform for enterprise backup management2014 IEEE Network Operations and Management Symposium (NOMS)10.1109/NOMS.2014.6838291(1-7)Online publication date: May-2014
https://doi.org/10.1109/NOMS.2014.6838291

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