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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation
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Optimizing Cloud-Service Performance: Efficient Resource Provisioning via Optimal Workload Allocation

机译：优化云服务性能：通过优化工作负载分配进行有效的资源配置

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摘要

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers’ requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloud-service performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed max-min-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisioning problem in the cloud is also studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy is proposed for serving dynamically arriving customer requests. These findings can be used by businesses to build a better understanding of how much virtual resource in the cloud they may need to meet customers’ expectations subject to cost constraints.
机译：云计算在商业世界中被广泛接受和利用。从利用云的企业的角度来看，通过实现服务级别目标来满足客户的需求至关重要。因此，准确表征和优化云服务性能的能力非常重要。本文提出了一种随机多租户框架，以在由异构虚拟机组成的云基础架构中对客户请求的服务进行建模。两个云服务性能指标在数学上具有特征，即封闭形式的客户请求的随机响应时间的百分比和均值。基于提出的多租户框架，然后设计了一种工作负载分配算法，称为最大-最小-云算法，以优化云服务的性能。还给出了最大-最小-云算法的严格最优性证明。此外，还根据最大-最小-云算法研究了云中的资源供应问题。特别是，提出了一种有效的资源供应策略，用于服务于动态到达的客户请求。企业可以利用这些发现来更好地了解在成本约束下他们可能需要多少虚拟资源才能满足客户的期望。

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