Optimizing multi-deployment on clouds by means of self-adaptive prefetching

Bogdan Nicolae 1, 2 Franck Cappello 1, 2 Gabriel Antoniu 3
1 GRAND-LARGE - Global parallel and distributed computing
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LIFL - Laboratoire d'Informatique Fondamentale de Lille, LRI - Laboratoire de Recherche en Informatique
3 KerData - Scalable Storage for Clouds and Beyond
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : With Infrastructure-as-a-Service (IaaS) cloud economics getting increasingly complex and dynamic, resource costs can vary greatly over short periods of time. Therefore, a critical issue is the ability to deploy, boot and terminate VMs very quickly, which enables cloud users to exploit elasticity to find the optimal trade-off between the computational needs (number of resources, usage time) and budget constraints. This paper proposes an adaptive prefetching mechanism aiming to reduce the time required to simultaneously boot a large number of VM instances on clouds from the same initial VM image (multi-deployment). Our proposal does not require any foreknowledge of the exact access pattern. It dynamically adapts to it at run time, enabling the slower instances to learn from the experience of the faster ones. Since all booting instances typically access only a small part of the virtual image along almost the same pattern, the required data can be pre-fetched in the background. Large scale experiments under concurrency on hundreds of nodes show that introducing such a prefetching mechanism can achieve a speed-up of up to 35% when compared to simple on-demand fetching.
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Bogdan Nicolae, Franck Cappello, Gabriel Antoniu. Optimizing multi-deployment on clouds by means of self-adaptive prefetching. Euro-Par '11: Proc. 17th International Euro-Par Conference on Parallel Processing, Aug 2011, Bordeaux, France. pp.503-513, ⟨10.1007/978-3-642-23400-2_46⟩. ⟨inria-00594406⟩

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