Workload-aware live storage migration for clouds

  • Authors:

  • Jie Zheng;Tze Sing Eugene Ng;Kunwadee Sripanidkulchai

  • Affiliations:

  • Rice University, Houston, USA;Rice University, Houston, USA;National Electronics and Computer Technology Center, Pathumthani, Thailand

  • Venue:
  • Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
  • Year:
  • 2011

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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.