考虑折扣的在线云资源分配竞争策略:二维停车许可证问题

Xinhui Hu, Arne Ludwig, A. Richa, S. Schmid
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引用次数: 22

摘要

云计算预示着一个时代的到来,在这个时代,资源可以以在线的方式弹性地伸缩。本文采用竞争分析方法,研究了价格折扣条件下具有成本效益的云资源分配算法。我们证明,对于单个资源,在线资源租赁问题可以看作是经典在线停车许可证问题的二维变体,并相应地正式引入PPP2问题。我们的主要贡献是PPP2的在线算法,它实现了k的确定性竞争比(在一组特定的假设下),其中k是资源束的数量。这几乎是最优的,因为我们也证明了任何确定性在线算法的k/3的下界。我们的在线算法使用了最优离线算法,这可能是独立的兴趣,因为它是停车许可证问题的一维和二维版本的第一个最优离线算法。最后,我们证明了我们的算法和结果也可以推广到多个资源(即多维停车许可证问题)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Competitive Strategies for Online Cloud Resource Allocation with Discounts: The 2-Dimensional Parking Permit Problem
Cloud computing heralded an era where resources can be scaled up and down elastically and in an online manner. This paper initiates the study of cost-effective cloud resource allocation algorithms under price discounts, using a competitive analysis approach. We show that for a single resource, the online resource renting problem can be seen as a 2-dimensional variant of the classic online parking permit problem, and we formally introduce the PPP2 problem accordingly. Our main contribution is an online algorithm for PPP2 which achieves a deterministic competitive ratio of k (under a certain set of assumptions), where k is the number of resource bundles. This is almost optimal, as we also prove a lower bound of k/3 for any deterministic online algorithm. Our online algorithm makes use of an optimal offline algorithm, which may be of independent interest since it is the first optimal offline algorithm for the 1D and 2D versions of the parking permit problem. Finally, we show that our algorithms and results also generalize to multiple resources (i.e., Multi-dimensional parking permit problems).
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