Bursting the Cloud Data Bubble: Towards Transparent Storage Elasticity in IaaS Clouds

Abstract : Storage elasticity on IaaS clouds is an important feature for data-intensive workloads: storage requirements can vary greatly during application runtime, making worst-case over-provisioning a poor choice that leads to unnecessarily tied-up storage and extra costs for the user. While the ability to adapt dynamically to storage requirements is thus attractive, how to implement it is not well understood. Current approaches simply rely on users to attach and detach virtual disks to the virtual machine (VM) instances and then manage them manually, thus greatly increasing application complexity while reducing cost efficiency. Unlike such approaches, this paper aims to provide a transparent solution that presents a unified storage space to the VM in the form of a regular POSIX file system that hides the details of attaching and detaching virtual disks by handling those actions transparently based on dynamic application requirements. The main difficulty in this context is to understand the intent of the application and regulate the available storage in order to avoid running out of space while minimizing the performance overhead of doing so. To this end, we propose a storage space prediction scheme that analyzes multiple system parameters and dynamically adapts monitoring based on the intensity of the I/O in order to get as close as possible to the real usage. We show the value of our proposal over static worst-case over-provisioning and simpler elastic schemes that rely on a reactive model to attach and detach virtual disks, using both synthetic benchmarks and real-life data-intensive applications. Our experiments demonstrate that we can reduce storage waste/cost by 30-40% with only 2-5% performance overhead.
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Submitted on : Monday, February 17, 2014 - 10:02:51 AM
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Bogdan Nicolae, Kate Keahey, Pierre Riteau. Bursting the Cloud Data Bubble: Towards Transparent Storage Elasticity in IaaS Clouds. IPDPS'14: The 28th IEEE International Parallel and Distributed Processing Symposium, May 2014, Phoenix, United States. ⟨10.1109/IPDPS.2014.25⟩. ⟨hal-00947599⟩

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