Abstract
Currently in large-scale scientific experiments, scientists often submit scientific workflow jobs at different time. From the view of system, the entire workload is a stream of jobs submitted at an unpredictable time and different job has different priority and deadline. Moreover the cost of performing these jobs cannot exceed a certain budget constraint. Therefore how to perform scientific workflow applications efficiently in cloud has become the urgent problem. However most of existing work didn't consider unpredictable submission time of jobs, as well as budget and deadline constrains. In this paper, we design an elastic resource provisioning and task scheduling mechanism to perform scientific workflows in cloud. Our goal is to complete as many high-priority workflows as possible under budget and deadline constrains. This mechanism consists of three phases: workflow preprocessing, elastic resource provisioning and task scheduling. We perform evaluation with real AMS experiment scientific computing data under different budget constraints. We also consider inaccurate task execution time, VM provisioning delays and task failures in evaluation. The results show that our mechanism achieves a better performance than these reference mechanisms. In addition, the inaccurate task execution time, VM provisioning delays, and task failures do not bring significant impact to mechanism's performance.
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