A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing

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 sol...

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Published inEngineering optimization Vol. 54; no. 12; pp. 1999 - 2016
Main Authors Rana, Nadim, Abd Latiff, Muhammad Shafie, Abdulhamid, Shafi'i Muhammad, Misra, Sanjay
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.12.2022
Taylor & Francis Ltd
Subjects
Algorithms
Cloud computing
differential evolution
Entrapment
Evolutionary computation
Heuristic methods
metaheuristic algorithm
Multiple objective analysis
Optimization
Scheduling
Virtual environments
Virtual machine scheduling
whale optimization algorithm
Online AccessGet full text
ISSN0305-215X
1026-745X
1029-0273
1029-0273
DOI10.1080/0305215X.2021.1969560

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Abstract 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.
AbstractList 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.
Author Misra, Sanjay
Abdulhamid, Shafi'i Muhammad
Abd Latiff, Muhammad Shafie
Rana, Nadim
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Snippet Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it is classed as an...
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SubjectTerms Algorithms
Cloud computing
differential evolution
Entrapment
Evolutionary computation
Heuristic methods
metaheuristic algorithm
Multiple objective analysis
Optimization
Scheduling
Virtual environments
Virtual machine scheduling
whale optimization algorithm
Title A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing
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