Performance analysis of parallel I/O scheduling approaches on cluster computing systems

As computation and communication hardware performance continue to rapidly increase, I/O represents a growing fraction of application execution time. This gap between the I/O subsystem and others is expected to increase in future since I/O performance is limited by physical motion. Therefore, it is imperative that novel techniques for improving I/O performance be developed. Parallel I/O is a promising approach to alleviating this bottleneck. However, very little work exist with respect to scheduling parallel I/O operations explicitly. In this paper, we address the problem of effective management of parallel I/O in cluster computing systems by using appropriate I/O scheduling strategies. We propose two new I/O scheduling algorithms and compare them with two existing scheduling Approaches. The preliminary results show that the proposed policies outperform existing policies substantially.