Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds
Stochastic Modeling and Performance Analysis of Migration-Enabled and Error-Prone Clouds
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支持迁移且容易出错的云的随机建模和性能分析
DOI：
10.1109/tii.2015.2405792
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发表时间：
2015-02
期刊：
影响因子：
--
通讯作者：
庞善臣
中科院分区：
文献类型：
--
作者：
夏云霓;周孟初;罗辛;庞善臣
云计算是一个有希望的范式，能够通过外包和虚拟化合理化计算资源的使用。虚拟化允许在VM MA管理的更少的物理系统之上实例化虚拟机（VM）
Cloud computing is a promising paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Virtualization allows to instantiate virtual machines (VMs) on top of fewer physical systems managed by a VM manager. Performance evaluation of clouds is required to evaluate and quantify the cost-benefit of a strategy portfolio and the quality of service (QoS) experienced by end-users. Such evaluation is not feasible by means of simulation or on-the-field measurement, due to the great scale of parameter spaces that have to be traversed. In this study, we present a stochastic-queuing-network-based approach to performance analysis of migration-enabled clouds in error-prone environment. Several performance metrics are defined and evaluated: utilization, expected task completion time, and task rejection rate under different load conditions and error intensities. To validate the proposed approach, we obtain experimental performance data through a real-world cloud and conduct a confidence-interval analysis. The analysis results suggest the perfect coverage of theoretical performance results by corresponding experimental confidence intervals.
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发表时间：
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期刊：
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影响因子：
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