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- W4214873985 abstract "Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions." @default.
- W4214873985 created "2022-03-05" @default.
- W4214873985 creator A5029130971 @default.
- W4214873985 date "2021-12-10" @default.
- W4214873985 modified "2023-09-28" @default.
- W4214873985 title "A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud Data Centers" @default.
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- W4214873985 doi "https://doi.org/10.1109/paap54281.2021.9720442" @default.
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