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Sector and Sphere: The Design and Implementation of a High-Performance Data Cloud

Yunhong Gu and Robert L. Grossman
Philosophical Transactions: Mathematical, Physical and Engineering Sciences
Vol. 367, No. 1897, Crossing Boundaries: Computational Science, e-Science and Global e-Infrastructure I. Selected Papers from the UK e-Science All Hands Meeting 2008 (Jun. 28, 2009), pp. 2429-2445
Published by: Royal Society
Stable URL: http://www.jstor.org/stable/40485591
Page Count: 17
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Sector and Sphere: The Design and Implementation of a High-Performance Data Cloud
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Abstract

Cloud computing has demonstrated that processing very large datasets over commodity clusters can be done simply, given the right programming model and infrastructure. In this paper, we describe the design and implementation of the Sector storage cloud and the Sphere compute cloud. By contrast with the existing storage and compute clouds, Sector can manage data not only within a data centre, but also across geographically distributed data centres. Similarly, the Sphere compute cloud supports user-defined functions (UDFs) over data both within and across data centres. As a special case, MapReduce-style programming can be implemented in Sphere by using a Map UDF followed by a Reduce UDF. We describe some experimental studies comparing Sector/Sphere and Hadoop using the Terasort benchmark. In these studies, Sector is approximately twice as fast as Hadoop. Sector/Sphere is open source.

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