Handling uncertainty in cloud resource management using fuzzy Bayesian networks
Handling uncertainty in cloud resource management using fuzzy Bayesian networks
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DOI：
10.1109/fuzz-ieee.2015.7337979
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Fahimeh Ramezani;M. Naderpour;Jie Lu
中科院分区：
文献类型：
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作者：
Fahimeh Ramezani;M. Naderpour;Jie Lu
云服务的成功关键取决于虚拟化资源的有效管理。本文旨在设计和实现一种决策支持方法，以处理资源管理的不确定性，从云提供商的角度来看，使底层的复杂性，自动化资源配置和控制客户端感知的服务质量。该论文包括一个概率决策模块，该模块依赖于模糊贝叶斯网络来确定云基础设施（包括物理机和虚拟机）的当前状况，并预测不久的将来的状态，这将有助于虚拟机管理程序迁移或扩展虚拟机，以减少执行时间并满足服务质量要求。首先，提出了资源管理的框架。其次，开发了决策模块。最后，通过一系列实验来研究该模块的性能。实验结果验证了该模块原型的有效性。
The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.