International audienceThis article presents our approach to provide input/output (I/O) scheduling with double adaptivity: to applications and devices. In high-performance computing environments, parallel file systems provide a shared storage infrastructure to applications. In the situation where multiple applications access this shared infrastructure concurrently, their performance can be impaired because of interference. Our work focuses on I/O scheduling as a tool to improve performance by alleviating interference effects. The role of the I/O scheduler is to decide the order in which applications' requests must be processed by the parallel file system's servers, applying optimizations to adjust the resulting access pattern for improved performance. Our approach to improve I/O scheduling results is based on using information from applications' access patterns and storage devices' sensitivity to access sequentiality. We have applied machine learning to provide the ability to automatically select the best scheduling algorithm for each situation. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Our results evidence that both aspects – applications and storage devices – are essential to make good scheduling decisions
In this paper, we improve the performance of server-side I/O scheduling on parallel file systems by transparently including information about the applications' access patterns. Server-side I/O scheduling is a valuable tool on multiapplication scenarios, where the applications' spatial locality suffers from interference caused by concurrent accesses to the file system. We present AGIOS, an I/O scheduling library for parallel file systems. We guide scheduler's decisions by including information about the applications' future requests. This information is obtained from traces generated by the scheduler itself, without changes in application or file system. Our approach shows performance improvements under different workloads of 46.3% on average when compared to a scenario without an I/O scheduler, and of 25.1% when compared to a scheduler which does not use information about future accesses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.