Exploring Portfolio Scheduling for Long-term Execution of Scientific Workloads in IaaS Clouds

Provided by: National University of Defense Technology
Topic: Cloud
Format: PDF
Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this paper, the authors introduce an abstract model to explore this selection problem.

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