Recent studies have found cloud environments increasingly appealing for executing HPC applications, including tightly coupled parallel simulations. While public clouds offer elastic, on-demand resource provisioning and pay-as-you-go pricing, individual users setting up their on-demand virtual clusters may not be able to take full advantage of common cost-saving opportunities, such as reserved instances.In this paper, we propose a Semi-Elastic Cluster (SEC) computing model for organizations to reserve and dynamically resize a virtual cloud-based cluster. We present a set of integrated batch scheduling plus resource scaling strategies uniquely enabled by SEC, as well as an online reserved instance provisioning algorithm based on job history. Our trace-driven simulation results show that such a model has a 61.0% cost saving than individual users acquiring and managing cloud resources without causing longer average job wait time. Meanwhile, the overhead of acquiring/maintaining shared cloud instances is shown to take only a few seconds.
Abstract. The FCFS-based backfill algorithm is widely used in scheduling high-performance computer systems. The algorithm relies on runtime estimate of jobs which is provided by users. However, statistics show the accuracy of user-provided estimate is poor. Users are very likely to provide a much longer runtime estimate than its real execution time. In this paper, we propose an aggressive backfilling approach with checkpoint based preemption to address the inaccuracy in user-provided runtime estimate. The approach is evaluated with real workload traces. The results show that compared with the FCFS-based backfill algorithm, our scheme improves the job scheduling performance in waiting time, slowdown and mean queue length by up to 40%. Meanwhile, only 4% of the jobs need to perform checkpoints.
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