Rethinking Node Allocation Strategy for Data-intensive Applications in Consideration of Spatially Bursty I/O

Abstract

Job scheduling in HPC systems by default allocate adjacent compute nodes for jobs for lower communication overhead. However, it is no longer applicable to data-intensive jobs running on systems with I/O forwarding layer, where each I/O node performs I/O on behalf of a subset of compute nodes in the vicinity. Under the default node allocation strategy a job's nodes are located close to each other and thus it only uses a limited number of I/O nodes. Since the I/O activities of jobs are bursty, at any moment only a minority of jobs in the system are busy processing I/O. Consequently, the bursty I/O traffic in the system is also concentrated in space, making the load on I/O nodes highly unbalanced. In this paper, we use the job logs and I/O traces collected from Tianhe-1A to quantitatively analyze the two causes of spatially bursty I/O, including uneven I/O traffic of job's processes and uneven distribution of job's nodes. Based on the analysis we propose a node allocation strategy that takes account of processes' different amounts of I/O traffic, so that the I/O traffic can be processed by more I/O nodes more evenly. Our evaluations on Tianhe-1A with synthetic benchmarks and realistic applications show that the proposed strategy can further exploit the potential of I/O forwarding layer and promote the I/O performance.