Dubois Daniel J, Casale Giuliano
Department of Computing, Imperial College London, London, UK.
Cluster Comput. 2016;19(2):893-909. doi: 10.1007/s10586-016-0568-7. Epub 2016 Apr 23.
The spot instance model is a virtual machine pricing scheme in which some resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose OptiSpot, a heuristic to automate application deployment decisions on cloud providers that offer the spot pricing model. In particular, with our approach it is possible to determine: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use to minimize the total cost while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.
竞价型实例模型是一种虚拟机定价方案，其中云提供商的一些资源会提供给出价最高者。这导致了竞价价格的形成，其波动可能会使客户被其他用户出价超过，从而失去他们租用的虚拟机。在本文中，我们提出了OptiSpot，这是一种启发式方法，用于在提供竞价定价模型的云提供商上自动进行应用程序部署决策。具体而言，通过我们的方法，可以确定：（i）为运行云应用程序租用哪些资源以及多少资源，（ii）如何将应用程序组件映射到租用的资源上，以及（iii）使用哪些竞价价格出价来在保持可接受性能水平的同时最小化总成本。为了推动决策制定，我们的算法将应用程序的多类排队网络模型与一个马尔可夫模型相结合，该马尔可夫模型描述了竞价价格的随机演变及其对虚拟机可靠性的影响。我们使用为一个实际企业应用程序开发的模型以及亚马逊EC2竞价型实例价格的历史记录表明，我们的启发式方法找到了确实能保证所需性能水平的低成本解决方案。我们将启发式方法的性能与非线性规划的性能进行了比较，结果表明它显著加快了低成本最优解的寻找速度。
