亚马逊EC2云现货实例最优竞价策略研究

Shaojie Tang, Jing Yuan, Xiangyang Li
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引用次数: 100

摘要

随着最近在Amazon Elastic Compute Cloud (EC2)中引入Spot实例,用户可以竞标资源,从而控制可靠性与货币成本之间的平衡。在此模式下处理成本-可靠性权衡的机制和工具对于寻求在保持高可靠性的同时降低成本的用户非常有价值。在本文中,我们提出了一套投标策略,以最小化资源供应的成本和波动性。从本质上讲,为了推导出最优的竞价策略,我们将这个问题表述为约束马尔可夫决策过程(CMDP)。在此模型的基础上,通过线性规划得到最优的随机竞价策略。使用真实的实例价格跟踪和工作负载模型,我们在货币成本和工作完成时间方面比较了几种自适应检查点方案。我们评估了我们的模型,并演示了用户应该如何在现货实例上进行最佳出价,以达到不同的目标,并具有所需的置信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Optimal Bidding Strategy for Amazon EC2 Cloud Spot Instance
With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies to minimize the cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance Price traces and workload models, we compare several adaptive check-pointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.
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