期刊
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
卷 27, 期 7, 页码 1915-1928出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2015.2476459
关键词
Cloud computing; job scheduling; resource provisioning; semi-elastic cluster; trace-driven simulation
资金
- Chinese National High-Tech Research and Development Plan (863 project) [2012AA01A302]
- NSFC [61232008, 61472201]
- Research Fund for Taishan Scholar Project of Shandong Province
- US National Science Foundation (NSF) [0915861, 0937908, 0958311, 1318564]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0937908] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1318564, 0958311] Funding Source: National Science Foundation
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0915861] Funding Source: National Science Foundation
Recent studies have found cloud environments increasingly appealing for executing HPC applications, including tightly coupled parallel simulations. At the same time, 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 percent cost saving than individual users acquiring and managing cloud resources without causing longer average job wait time. Moreover, to exploit the advantages of different public clouds, we also extend SEC to a multi-cloud environment, where SEC can get a lower cost than on any single cloud. We design and implement a prototype system of the SEC model and evaluate it in terms of management overhead and average job wait time. Experimental results show that the management overhead is negligible with respect to the job wait time.
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