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• W2941753313 abstract "Abstract In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively." @default.
• W2941753313 created "2019-05-03" @default.
• W2941753313 creator A5015886925 @default.
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• W2941753313 date "2019-04-09" @default.
• W2941753313 modified "2023-10-02" @default.
• W2941753313 title "Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing" @default.
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• W2941753313 cites W1967910577 @default.
• W2941753313 cites W1971650146 @default.
• W2941753313 cites W2002836673 @default.
• W2941753313 cites W2022796131 @default.
• W2941753313 cites W2034609836 @default.
• W2941753313 cites W2047095174 @default.
• W2941753313 cites W2055209518 @default.
• W2941753313 cites W2062059020 @default.
• W2941753313 cites W2062775008 @default.
• W2941753313 cites W2081574853 @default.
• W2941753313 cites W2098909968 @default.
• W2941753313 cites W2099692426 @default.
• W2941753313 cites W2110374615 @default.
• W2941753313 cites W2112912151 @default.
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• W2941753313 cites W2143381319 @default.
• W2941753313 cites W2146439308 @default.
• W2941753313 cites W2148459868 @default.
• W2941753313 cites W2150923807 @default.
• W2941753313 cites W2151554678 @default.
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• W2941753313 cites W2597822521 @default.
• W2941753313 cites W2606276573 @default.
• W2941753313 cites W2613383920 @default.
• W2941753313 cites W2618719420 @default.
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• W2941753313 doi "https://doi.org/10.1186/s13673-019-0174-9" @default.
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