Virtualization is a crucial technology of cloud computing to enable the flexible use of a significant amount of distributed computing services on a pay-as-you-go basis. As the service demand continuingly increases to a global scale, efficient virtual machine consolidation becomes more and more imperative. Existing heuristic algorithms targeted mostly at minimizing either the rate of service level agreement violations or the energy consumption of the cloud. However, the communication overhead among different virtual machines and the decision time of virtual machine consolidation are rarely considered. To reduce both the over-utilized nodes and the under-utilized nodes with the consideration of migration cost, communication overhead, and energy consumption, this paper presents a new iterative budget algorithm in which a budget heuristic and a multi-stage selection strategy are designed to find suitable migration objects and targets simultaneously. Experiments show that the proposed algorithm provides a substantial improvement over other typical heuristics and metaheuristic algorithms in reducing the energy consumption, the number of migrated virtual machines, the overall communication overhead, as well as the decision time.

中文翻译：

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云计算环境下动态虚拟机整合的迭代预算算法（2017年12月修订）

虚拟化是云计算的一项关键技术，可以在即用即付的基础上灵活使用大量分布式计算服务。随着服务需求在全球范围内不断增加，高效的虚拟机整合变得越来越必要。现有的启发式算法主要针对最小化服务水平协议违规率或云的能源消耗。然而，很少考虑不同虚拟机之间的通信开销和虚拟机整合的决策时间。在考虑迁移成本、通信开销和能源消耗的情况下，减少过度使用和未充分使用的节点，本文提出了一种新的迭代预算算法，其中设计了预算启发式和多阶段选择策略，以同时找到合适的迁移对象和目标。实验表明，与其他典型启发式和元启发式算法相比，所提出的算法在降低能耗、迁移的虚拟机数量、整体通信开销以及决策时间方面提供了实质性的改进。