TomusBlobs: Towards Communication-Efficient Storage for MapReduce Applications in Azure

Radu Tudoran 1 Alexandru Costan 1 Gabriel Antoniu 1, * Hakan Soncu 2
* Corresponding author
1 KerData - Scalable Storage for Clouds and Beyond
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : The emergence of cloud computing brought the opportunity to use large-scale computational infrastructures for a broad spectrum of applications and users. As the cloud paradigm gets attractive for the "elasticity" in resource usage and associated costs (the users only pay for resources actualy used), cloud applications still suffer from the high latencies and low performance of cloud storage services. Enabling high- throughput massive data processing on cloud data becomes a critical issue, as it impacts the overall application performance. In this paper we address the above challenge at the level of the cloud storage. We introduce a concurrency-optimized data storage system which federates the virtual disks associated to VMs. We demonstrate the performance of our solution for efficient data-intensive processing on commercial clouds by build- ing an optimized prototype MapReduce framework for Azure that leverages the benefits of our storage solution. We perform extensive microbenchmarks as well as experiments with real- world applications: they demonstrate that our solution brings substantial benefits to data intensive applications compared to approaches relying on state-of-the-art cloud object storage
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Contributor : Gabriel Antoniu <>
Submitted on : Wednesday, February 15, 2012 - 11:36:12 PM
Last modification on : Friday, November 16, 2018 - 1:38:37 AM


  • HAL Id : hal-00670725, version 1


Radu Tudoran, Alexandru Costan, Gabriel Antoniu, Hakan Soncu. TomusBlobs: Towards Communication-Efficient Storage for MapReduce Applications in Azure. 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid'2012), May 2012, Ottawa, Canada. ⟨hal-00670725⟩



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