用于IaaS云成本优化的在线多实例获取

N. Alouane, J. Abouchabaka, N. Rafalia
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引用次数: 0

摘要

Amazon Ec2服务提供了两种不同的实例购买选项。用户可以使用按需计划运行实例,并仅为所产生的实例小时付费,也可以长期租用实例,同时大幅减少(最多减少60%)。这些用户面临的主要问题之一是成本管理。如何在不了解未来需求的情况下,动态结合这两种方案,服务零星的工作量?文献中的许多策略要求使用精确的历史工作负载作为参考,或者依赖于对未来工作负载的长期预测。与现有的研究不同,我们针对多斜率情况提出了两种实用的在线确定性算法,与最优离线算法相比,它们的成本分别不超过1+1/1−α和2/1−α,其中α是保留实例提供比按需计划的最大节省率。
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Online multi-instance acquisition for cost optimization in IaaS Clouds
Amazon Ec2 service offers two diverse instance purchasing options. Users can either run instances by using on-demand plan and pay only for the incurred instance-hours, or by renting instances for a long period, while taking advantage of significant reductions (up to 60%). One of the major problems facing these users is cost management. How to dynamically combine between these two options, to serve sporadic workload, without knowledge of future demands? Many strategies in the literature, require either using exact historic workload as a reference or relying on long-term predictions of future workload. Unlike existing works we propose two practical online deterministic algorithms for the multi-slope case, that incur no more than 1+1/1−α and 2/1−α respectively, compared to the cost obtained from an optimal offline algorithm, where α is the maximum saving ratio of a reserved instance offer over on-demand plan.
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