This extended abstract presents and analyses three decentralized parallel I/O heuristics implemented in a parallel file system for clusters of commodity computers (Clusterfile). The measurements in a real environment show that the performance of parallel file access may vary with as much as 68% with the employed heuristic and with the schedule block granularity. The performance of applications accessing large data sets is often limited by the speed of I/O subsystems. On one hand, this limitation comes from the ever increasing discrepancy between processor, memory speed and magnetic disks. On the other hand, the potential for parallelism existent in clusters of commodities of computers and supercomputers is not always fully exploited by the I/O system software, like the parallel file systems [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] and libraries [12, 13] . These systems employ mechanisms such as striping a file over several independent disks managed by I/O nodes and allowing parallel file access from several compute nodes. use collective buffers in order to gather the requests from compute nodes before sending them to disks. For disk-directed I/O the collective buffers reside at I/O nodes, whereas for two-phase I/O at intermediary compute nodes. Both of these methods describe how the data flows through the system between compute nodes and I/O nodes, but do not say anything about the order in which requests are sent between parallel running compute nodes and I/O nodes. However, an improper request ordering may cause problems like load imbalance or resource contention, which may have a tremendous impact on performance. To best of our knowledge it does not exist any in-depth analysis of the importance of the requests scheduling for I/O the performance of a parallel file system. The parallel I/O scheduling problem is not new. It was formulated by Jain and et al. [19] as follows. Given n p compute nodes, n IOS I/O servers and a set of requests for transfers of the same length between compute nodes and I/O servers and assuming that a compute node and an I/O server can perform exactly one transfer at any given time,
Decentralized I/O scheduling strategies between computer nodes and I/O servers for parallel file systems are developed in REF .
On evaluating decentralized parallel I / O scheduling strategies for parallel file systems
VECPAR
Computer Science