如何竞标云

Liang Zheng, Carlee Joe-Wong, C. Tan, M. Chiang, Xinyu Wang
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引用次数: 175

摘要

亚马逊的弹性计算云(EC2)使用基于拍卖的现货定价来出售备用容量,允许用户以极低的价格竞标云资源。亚马逊动态设置现货价格,并接受高于此价格的用户出价。出价较低的作业(包括那些已经在运行的作业)被中断,必须等待较低的现货价格才能恢复。因此,现货定价提出了两个基本问题:供应商如何设定价格,以及用户应该出价多少?计算用户的投标策略尤其具有挑战性:较高的投标价格降低了中断的可能性,从而减少了从中断中恢复的额外时间,但可能会增加用户的成本。我们通过三个步骤解决了这些问题:(1)对云提供商的现货价格设置进行建模,并将模型与历史报价进行匹配;(2)针对不同的工作要求和中断开销推导出最优的竞标策略;(3)将这些策略应用于具有不同中断开销的主节点和从节点的MapReduce作业。我们在EC2上针对各种作业大小和实例类型运行了我们的策略,结果表明,与按需定价相比,现货定价降低了90%的用户成本,完成时间也有所增加。
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How to Bid the Cloud
Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.
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