预留实例市场中动态实例分配的在线机制

Min Yao, Chuang Lin
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引用次数: 6

摘要

作为Amazon提供的定价模型之一,保留实例使用户能够为其EC2实例保留容量,并降低其平均实例成本。为了吸引更多的用户采用保留实例,Amazon提供了一个名为reserved instance Marketplace的平台,让用户可以灵活地在需求发生变化时出售剩余的保留实例。然而,目前保留实例市场内部的交易机制要求保留实例卖家按月出售其保留容量,并自行设定预付费用,这不够灵活,对于经验不足的卖家来说,很难指定合适的预付费用。为了解决这个问题,本文提出了一种保留实例市场的在线机制。我们的在线机制试图在不需要预先指定费用的情况下,通过在不同的买家之间动态分配预留容量来最大化卖方的收益。从理论上证明了该机制下在线分配算法的竞争比在最优竞争比的一个小常数因子内。为了评估我们的在线机制的性能,我们对来自一个Google集群的合成数据和真实数据跟踪进行了模拟。仿真结果表明,在大多数情况下,我们的在线机制可以达到离线最优算法的55%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An online mechanism for dynamic instance allocation in reserved instance marketplace
As one of the pricing model offered by Amazon, reserved instance enables users to reserve capacities for their EC2 instances and lowers their average instance cost. To attract more users to adopt the reserved instance, Amazon has provided a platform named Reserved Instance Marketplace to give users the flexibility to sell the remainder of their reserved instances as their needs change. However, the present trading mechanism inside the Reserved Instance Marketplace requires the reserved instance sellers to sell their reserved capacities at month level and set the upfront fee by themselves, which is not flexible enough and hard for an inexperienced seller to specify a suitable upfront fee. To address this problem, this paper proposes an online mechanism for the Reserved Instance Marketplace. Our online mechanism tries to maximize the sellers' revenue by dynamically allocating the reserved capacities among various buyers without the need of specifying upfront fee in advance. The competitive ratio of the online allocation algorithm inside our mechanism is proved to be within a small constant factor of optimal competitive ratio in theory. To evaluate the performance of our online mechanism, we conduct simulations on synthetic data and real data trace from one of Google clusters. The simulation results show that our online mechanism can achieve at least 55% of the offline optimal algorithm in most cases.
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