IO-aware Job-Scheduling: Exploiting the Impacts of Workload Characterizations to select the Mapping Strategy

Abstract

In high performance, computing concurrent applications are sharing the same file system. However, the bandwidth which provides access to the storage is limited. Therefore, too many I/O operations performed at the same time lead to conflicts and performance loss due to contention. This scenario will become more common as applications become more data intensive. To avoid congestion, job-schedulers have to play an important role in selecting which application run concurrently. However I/O-aware mapping strategies need to be simple, robust and fast. Hence, in this article, we discuss two plain and practical strategies to mitigate I/O congestion. They are based on the idea of scheduling I/O access so as not to exceed some prescribed I/O bandwidth. More precisely, we compare two approaches: one grouping applications into packs that will be run independently (i.e., pack-scheduling), the other one scheduling greedily applications using a predefined order (i.e. list-scheduling). Results show that performances depend heavily on the I/O load and the homogeneity of the underlying workload. Finally, we introduce the notion of characteristic time that represents information on the average time between consecutive I/O transfers. We show that it could be important to the design of schedulers and that we expect it to be easily obtained by analysis tools.