Reconciling scratch space consumption, exposure, and volatility to achieve timely staging of job input data

Innovative scientific applications and emerging dense data sources are creating a data deluge for high-end computing systems. Processing such large input data typically involves copying (or staging) onto the supercomputer´s specialized high-speed storage, scratch space, for sustained high I/O throughput. The current practice of conservatively staging data as early as possible makes the data vulnerable to storage failures, which may entail re-staging and consequently reduced job throughput. To address this, we present a timely staging framework that uses a combination of job startup time predictions, user-specified intermediate nodes, and decentralized data delivery to coincide input data staging with job start-up. By delaying staging to when it is necessary, the exposure to failures and its effects can be reduced. Evaluation using both PlanetLab and simulations based on three years of Jaguar (No. 1 in Top500) job logs show as much as 85.9% reduction in staging times compared to direct transfers, 75.2% reduction in wait time on scratch, and 2.4% reduction in usage/hour.