Journal
ENGINEERING OPTIMIZATION
Volume 54, Issue 12, Pages 1999-2016Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2021.1969560
Keywords
Virtual machine scheduling; metaheuristic algorithm; whale optimization algorithm; differential evolution; cloud computing
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The study proposed a hybrid algorithm (M-WODE) based on evolutionary algorithm and whale optimization algorithm for solving virtual machine scheduling problems. Experimental results show that the algorithm outperformed previous algorithms in most cases in terms of makespan and cost trade-offs.
Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it is classed as an NP-hard optimization problem. Swarm-based metaheuristics, such as the whale optimization algorithm (WOA), have gained a notable reputation for solving optimization problems. The unique bubble-net hunting behaviour and fast convergence of the algorithm led to the development of a hybrid multi-objective whale optimization algorithm-based differential evolution (M-WODE) technique to solve the VM scheduling problem. The differential evolution (DE) strategy is used to replace the randomly generated solution produced by the WOA to ensure diversity in the solution and to strengthen the local search of the M-WODE. In addition, the DE technique is applied to the Pareto front produced by the WOA to escape local optima entrapment problems. The experimental results showed that the proposed M-WODE outperformed previous algorithms in most cases on makespan and the cost trade-off.
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