High Throughput Data-Compression for Cloud Storage

Bogdan Nicolae 1
1 KerData - Scalable Storage for Clouds and Beyond
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique
Abstract : As data volumes processed by large-scale distributed data-intensive applications grow at high-speed, an increasing I/O pressure is put on the underlying storage service, which is responsible for data management. One particularly difficult challenge, that the storage service has to deal with, is to sustain a high I/O throughput in spite of heavy access concurrency to massive data. In order to do so, massively parallel data transfers need to be performed, which invariably lead to a high bandwidth utilization. With the emergence of cloud computing, data intensive applications become attractive for a wide public that does not have the resources to maintain expensive large scale distributed infrastructures to run such applications. In this context, minimizing the storage space and bandwidth utilization is highly relevant, as these resources are paid for according to the consumption. This paper evaluates the trade-off resulting from transparently applying data compression to conserve storage space and bandwidth at the cost of slight computational overhead. We aim at reducing the storage space and bandwidth needs with minimal impact on I/O throughput when under heavy access concurrency. Our solution builds on BlobSeer, a highly parallel distributed data management service specifically designed to enable reading, writing and appending huge data sequences that are fragmented and distributed at a large scale. We demonstrate the benefits of our approach by performing extensive experimentations on the Grid'5000 testbed.
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Bogdan Nicolae. High Throughput Data-Compression for Cloud Storage. Globe '10: Proceedings of the 3rd International Conference on Data Management in Grid and P2P Systems, Sep 2010, Bilbao, Spain. pp.1-12, ⟨10.1007/978-3-642-15108-8_1⟩. ⟨inria-00490541⟩

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