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Load Balancing using Grid-based Peer-to-Peer Parallel I/O

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2 Author(s)
Yijian Wang ; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115. ; David Kaeli

In the area of grid computing, there is a growing need to process large amounts of data. To support this trend, we need to develop efficient parallel storage systems that can provide for high performance for data-intensive applications. In order to overcome I/O bottlenecks and to increase I/O parallelism, data streams need to be parallelized at both the application level and the storage device level. In this paper, we propose a novel peer-to-peer (P2P) storage architecture for MPI applications on grid systems. We first present an analytic model of our P2P storage architecture. Next, we describe a profile-guided data allocation algorithm that can increase the degree of I/O parallelism present in the system, as well as to balance I/O in a heterogeneous system. We present results on an actual implementation. Our experimental results show that by partitioning data across all available storage devices and carefully tuning I/O workloads in the grid system, our peer-to-peer scheme can deliver scalable high performance I/O that can address I/O-intensive workloads

Published in:

2005 IEEE International Conference on Cluster Computing

Date of Conference:

Sept. 2005