Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51 percent when spot and on-demand instances are considered and of up to 43.74 percent when self-owned instances are considered, compared to previously proposed or intuitive policies.

中文翻译：

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设计成本最优策略以通过在线学习利用 IaaS 云

许多企业拥有可用于计算任务的小型基础设施，但也经常从云中购买额外的计算资源。Amazon EC2 等云供应商提供两种类型的购买选项：按需实例和现货实例。由于租户的预算有限，无法满足他们的计算需求，因此他们必须确定如何购买不同的选项并以具有成本效益的方式使用它们（除了可能的自有实例），同时尊重他们的响应时间目标。在本文中，我们提出了一个框架来设计将自有实例、按需实例和现货实例分配给到达作业的策略。特别是，我们提出了一个接近最优的策略来确定自有实例的数量，并提出一个最优策略来确定每个时间单位购买的按需实例数量和竞标的现货实例数量。我们的策略依赖于少量参数，我们使用在线学习技术来推断它们的最佳值。通过数值模拟，我们展示了我们提出的策略的有效性，特别是它们在考虑现货和按需实例时实现了高达 64.51% 的成本降低，在考虑自有实例时实现了高达 43.74% 的成本降低，相比之下先前提出的或直观的政策。