A Server-Level Adaptive Data Layout Strategy for Parallel File Systems

Abstract

Parallel file systems are widely used for providing a high degree of I/O parallelism to mask the gap between I/O and memory speed. However, peak I/O performance is rarely attained due to complex data access patterns of applications. Based on the observation that the I/O performance of small requests is often limited by the request service rate, and the performance of large requests is limited by I/O bandwidth, we take into consideration both factors and propose a server-level adaptive data layout strategy. The proposed strategy adopts different stripe sizes for different file servers according to the data access characteristics on each individual server. We let the file servers that can fully utilize bandwidth hold more data, and the file servers that are limited with request service rate hold less data. As a result, heavy-load servers can offload some data accesses to light-load servers for potential improvement of I/O performance. We present a method to measure access cost for each data block and then utilize an equal-depth histogram approach to distributed data blocks across multiple servers adaptively, so as to balance data accesses on all file servers. Analytical and experimental results demonstrate that the proposed server-level adaptive layout strategy can improve I/O performance by as much as 80.3% and is more appropriate for applications with complex data access patterns.