Abstract
The energy consumption in a data center is a big issue as it is responsible for about half of the operational cost of the data centres. Thus, it is desirable to reduce the energy consumption in data centre. One of the most effective ways of cutting the energy consumption in a data centre is through server consolidation, which can be modelled as a virtual machine placement problem. Since virtual machines in a data centre may come and go at any time, the virtual machine placement problem is a dynamic one. As a result, a decrease-and-conquer dynamic genetic algorithm has been proposed for the dynamic virtual machine placement problem. The decrease-and-conquer strategy plays a very important role in the dynamic genetic algorithm as it directly affects the performance of the dynamic genetic algorithm. In this paper we propose three new decrease-and-conquer strategies and conduct an empirical study of the three new decrease-and-conquer strategies as well as the existing one being used in the decrease-and-conquer genetic algorithm. Through the empirical study we find one of the decrease-and-conquer strategy, namely new first-fit decreasing, is significantly better than the existing decrease-and-conquer strategy.
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Sonklin, C., Tang, M., Tian, YC. (2017). New Decrease-and-Conquer Strategies for the Dynamic Genetic Algorithm for Server Consolidation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_53
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DOI: https://doi.org/10.1007/978-3-319-70093-9_53
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