Self-Tuning Virtual Machines for Predictable eScience

Unpredictable access to batch-mode HPC resources is a significant problem for emerging dynamic data-driven applications. Although efforts such as reservation or queue-time prediction have attempted to partially address this problem, the approaches strictly based on space-sharing impose fundamental limits on real-time predictability. In contrast, our earlier work investigated the use of feedback-controlled virtual machines (VMs), a time-sharing approach, to deliver predictable execution. However, our earlier work did not fully address usability and implementation efficiency. This paper presents an online, software-only version of feedback controlled VM, called self-tuning VM, which we argue is a practical approach for predictable HPC infrastructure. Our evaluation using five widely-used applications show our approach is both predictable and practical: by simply running time-dependent jobs with our tool, we meet a jobpsilas deadline typically within 3% errors, and within 8% errors for the more challenging applications.