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首页> 外文会议>IEEE International Conference on Parallel and Distributed Systems >A Dynamic Hybrid Resource Provisioning Approach for Running Large-Scale Computational Applications on Cloud Spot and On-Demand Instances
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A Dynamic Hybrid Resource Provisioning Approach for Running Large-Scale Computational Applications on Cloud Spot and On-Demand Instances

机译：在云点和按需实例上运行大规模计算应用程序的动态混合资源配置方法

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Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.
机译：由于成本节省，弹性和可扩展性，在公共云中进行测试和执行大规模计算应用正在变得普遍。但是，如何提高可靠性并降低在公共云中运行大规模应用程序的成本仍然是一个很大的挑战。在本文中，我们分析了亚马逊弹性计算云（EC2）的定价方案，发现扰动效果是由于所需的斑点实例的数量较多，现场实例的价格可能严重影响。我们提出了一种动态方法，该方法在云计算实例的动态池中计划和运行大规模计算应用程序。我们使用混合实例，包括用于高优先级任务和备份的按需实例，以及用于正常计算任务的现场实例，以便进一步降低成本而不会显着增加完成时间。我们所提出的方法考虑了云实例的动态定价，降低了成本并容忍在公共云中运行大规模应用程序的故障。我们对可持续城市进行了实验测试和基于代理的可扩展复杂系统建模（S3）应用程序用于评估可扩展性，可靠性和节省成本。结果表明，我们提出的方法对于研究人员和用户来说，我们的提出方法是强大，灵活性，以进一步降低实际实践的成本。

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