Research Area:  Cloud Computing
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.
Author(s) Name:  Yuanjun Laili; Fei Tao; Fei Wang; Lin Zhang and Tingyu Lin
Journal name:  IEEE Transactions on Services Computing
Publisher name:  IEEE
Volume Information:   Volume: 14, Issue: 1, Jan.-Feb. 1 2021,Page(s): 30 - 43
Paper Link:   https://ieeexplore.ieee.org/abstract/document/8258952