Going Back and Forth: Efficient Virtual Machine Image Deployment and Snapshotting on IaaS Clouds

Bogdan Nicolae 1, * John Bresnahan 2 Kate Keahey 2 Gabriel Antoniu 1
* Corresponding author
1 KerData - Scalable Storage for Clouds and Beyond
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : Infrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we think of acquiring resources by introducing a simple change: allowing users to lease computational resources from the cloud provider's datacenter for a short time by deploying virtual machines (VMs) on those resources. This new model raises new challenges in the design and development of IaaS middleware. On of those challenges if the need to deploy a large number (hundreds or even thousands) of VM instances simultaneously. Once the VM instances are deployed, another challenge is to simultaneously take a snapshot of many images and transfer them to persistent storage to support management tasks, such as suspend-resume and migration. This paper addresses both challenges by proposing a series of optimization techniques minimizing resource consumption (execution time, network traffic and storage space) which translate into lower end-user costs. While conventional approaches transfer the whole VM image contents between the persistent storage service and the computing nodes, we propose a lazy transfer scheme based on object-versioning that transfers only the needed content on-demand: this greatly reduces total time for execution time, network traffic and storage space. We demonstrate these benefits through experiments operating on hundreds of nodes which show improvements in time to process application execution by a factor of up to 25, while at the same time reducing storage and bandwidth usage by as much as 90% when compared with conventional approaches.
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Bogdan Nicolae, John Bresnahan, Kate Keahey, Gabriel Antoniu. Going Back and Forth: Efficient Virtual Machine Image Deployment and Snapshotting on IaaS Clouds. [Research Report] RR-7482, INRIA. 2010. ⟨inria-00545232⟩

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