Virtual Machine and User Task Placement Algorithm to Minimize Energy Consumption 


Vol. 48,  No. 3, pp. 338-349, Mar.  2023
10.7840/kics.2023.48.3.338


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  Abstract

In the cloud system, the strategy for placing virtual machines on the optimal physical machine and the strategy for placing user tasks on the optimal virtual machine is important because it has a significant impact on e energy consumption in the data center. Existing studies have reduced wasted idle power by minimizing the total number of running physical machines. However, the energy consumption optimization model of the existing research has low accuracy because it calculates for a certain small time rather than the makespan of a physical machine. In addition, an optimization algorithm of existing research has a disadvantage that it converges to a local optimal solution. In this paper, we proposed an energy consumption model with higher accuracy than existing energy consumption model. We also proposed an optimization algorithm modified from a genetic algorithm that escapes the risk of local optimal solution convergence. The experiment was conducted through the CloudSim simulator. As a result, it was confirmed that the proposed method consumes energy more efficiently than the existing method.

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[IEEE Style]

Dong-wookWon and Hwa-sungKim, "Virtual Machine and User Task Placement Algorithm to Minimize Energy Consumption," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 3, pp. 338-349, 2023. DOI: 10.7840/kics.2023.48.3.338.

[ACM Style]

Dong-wookWon and Hwa-sungKim. 2023. Virtual Machine and User Task Placement Algorithm to Minimize Energy Consumption. The Journal of Korean Institute of Communications and Information Sciences, 48, 3, (2023), 338-349. DOI: 10.7840/kics.2023.48.3.338.

[KICS Style]

Dong-wookWon and Hwa-sungKim, "Virtual Machine and User Task Placement Algorithm to Minimize Energy Consumption," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 3, pp. 338-349, 3. 2023. (https://doi.org/10.7840/kics.2023.48.3.338)
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eISSN : 2287 - 3880