A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems
A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems
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DOI：
10.1109/tpds.2013.67
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发表时间：
2014-03
影响因子：
5.3
通讯作者：
Dario Bruneo
中科院分区：
文献类型：
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作者：
Dario Bruneo
云数据中心管理是一个关键问题，因为可以应用大量的异构策略，从虚拟机放置到与其他云的联合。云计算基础设施的性能评估需要预测和量化策略组合的成本效益以及用户体验的相应服务质量（QoS）。这种分析是不可行的模拟或现场实验，由于大量的参数，必须进行调查。在本文中，我们提出了一个分析模型，基于随机奖励网（SRN），这是可扩展的模型系统组成的数千个资源和灵活的代表不同的政策和云特定的战略。定义并评估了几个性能指标，以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。还提供弹性分析以考虑负载突发。最后，提出了一种通用的方法，从系统容量的概念，可以帮助系统管理人员在不同的工作条件下，适时地设置数据中心的参数。
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.