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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems
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A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems

机译：研究IaaS云计算系统中数据中心性能和QoS的随机模型

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摘要

Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of cloud computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.
机译：云数据中心管理是一个关键问题，因为可以应用多种多样的策略，范围从VM放置到与其他云的联合。需要对云计算基础架构进行性能评估，以预测和量化策略组合的成本效益以及用户体验到的相应服务质量（QoS）。由于必须研究大量参数，因此通过仿真或现场实验无法进行此类分析。在本文中，我们提出了一种基于随机奖励网（SRN）的分析模型，该模型既可扩展到由数千种资源组成的模型系统，又可以灵活地表示不同的策略和特定于云的策略。定义并评估了几个性能指标来分析云数据中心的行为：利用率，可用性，等待时间和响应能力。还提供了弹性分析，以考虑负载突发。最后，提出了一种通用方法，该方法从系统容量的概念开始，可以帮助系统管理员在不同的工作条件下适当地设置数据中心参数。

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