Autonomic Provisioning and Application Mapping on Spot Cloud Resources
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
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DOI：
10.1109/iccac.2015.21
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Daniel J. Dubois;G. Casale
中科院分区：
文献类型：
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作者：
Daniel J. Dubois;G. Casale
现货实例模型是一种虚拟机定价方案，其中云提供商的未使用资源提供给出价最高的投标人。这导致现货价格的形成，其波动可能会决定客户被其他用户出价过高，并失去他们租用的虚拟机。在本文中，我们提出了一个启发式自动化的决定：（一）哪些和多少资源租用，以运行云应用程序，（二）如何映射的应用程序组件租用的资源，以及（三）什么样的现货价格投标使用，以尽量减少总投标价格，同时保持可接受的性能水平。为了推动决策，我们的算法结合了应用程序的多类嵌入式网络模型与马尔可夫模型，描述了现货价格的随机演变及其对虚拟机可靠性的影响。我们使用一个为真实的企业应用程序开发的模型和Amazon EC2现货实例价格的历史跟踪，表明我们的启发式方法可以找到确实保证所需性能水平的低成本解决方案。我们的启发式方法的性能相比，非线性规划和显着加快低成本的最优解的发现。
The spot instance model is a virtual machine pricing scheme in which unused 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 a heuristic to automate the decision on: (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 in order to minimize the total bid price 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.