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Workload-aware live storage migration for clouds

Published:09 March 2011Publication History

ABSTRACT

The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This paper presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a fully implemented system on KVM and a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.

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    • Published in

      cover image ACM Conferences
      VEE '11: Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
      March 2011
      250 pages
      ISBN:9781450306874
      DOI:10.1145/1952682
      • cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 46, Issue 7
        VEE '11
        July 2011
        231 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2007477
        Issue’s Table of Contents

      Copyright © 2011 ACM

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      Publication History

      • Published: 9 March 2011

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