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Underutilization of computing resources and high power consumption are twoprimary challenges in the domain of Cloud resource management. This paper dealswith these challenges through offline, migration impact-aware, multi-objectivedynamic Virtual Machine (VM) consolidation in the context of large-scalevirtualized data center environments. The problem is formulated as an NP-harddiscrete combinatorial optimization problem with simultaneous objectives ofminimizing resource wastage, power consumption, and the associated VM migrationoverhead. Since dynamic VM consolidation through VM live migrations havenegative impacts on hosted applications performance and data center components,a VM live migration overhead estimation technique is proposed, which takes intoaccount pragmatic migration parameters and overhead factors. In order to tacklescalability issues, a hierarchical, decentralized dynamic VM consolidationframework is presented that helps to localize migration-related network trafficand reduce network cost. Moreover, a multi-objective, dynamic VM consolidationalgorithm is proposed by utilizing the Ant Colony Optimization (ACO)metaheuristic, with integration of the proposed VM migration overheadestimation technique. Comprehensive performance evaluation makes it evidentthat the proposed dynamic VM consolidation approach outpaces thestate-of-the-art offline, migration-aware dynamic VM consolidation algorithmacross all performance metrics by reducing the overall power consumption by upto 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Multi-objective, Decentralized Dynamic Virtual Machine Consolidation using ACO Metaheuristic in Computing Clouds