An optimal candidate selection model for self-acting load balancing of parallel file system
International Journal of High Performance Computing and Networking
A dynamic and adaptive load balancing strategy for parallel file system with large-scale I/O servers
Journal of Parallel and Distributed Computing
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With number of I/O servers increasing, the load imbalance among them becomes an emerging challenge in using high-end computing system effectively. But most of contemporary file systems like PVFS2 and Lustre lack load balancing to optimize parallel I/O performance when facing large number of I/O servers. To address this problem, this paper proposes self-acting load balancing for parallel file systems. The proposed self-acting load balancing builds on dynamic file migration, and borrows the ideas from intelligent computing to enable automatic load balancing. Furthermore, in order to support promising scalability, the self-acting load balancing is fundamentally based on distributed architecture, which also permits parallel file migrations to accelerate balancing progress. By taking PVFS2 as basic platform, we implement the self-acting load balancing, and the experiments show that PVFS2 with self-acting load balancing can reduce the average response time and improve performance of parallel I/O obviously compared with original PVFS2.