Abstract
Energy efficiency is crucial for the operation and management of cloud data centers, which are the foundation of cloud computing. Virtual machine (VM) placement plays a vital role in improving energy efficiency in data centers. The genetic algorithm (GA) has been extensively studied for solving the VM placement problem due to its ability to provide high-quality solutions. However, GA’s high computational demands limit further improvement in energy efficiency, where a fast and lightweight solution is required. This paper presents an adaptive population control scheme that enhances gene diversity through population control, adaptive mutation rate, and accelerated termination. Experimental results show that our scheme achieves a 17% faster acceleration and 49% fewer generations compared to the standard GA for energy-efficient VM placement in large-scale data centers.
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References
Alharbi, F., Tian, Y.C., Tang, M., Zhang, W.Z., Peng, C., Fei, M.: An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Syst. Appl. 120, 228–238 (2019)
Ding, Z., Tian, Y.C., Tang, M.: Efficient fitness function computation of genetic algorithm in virtual machine placement for greener data centers. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), pp. 181–186, Porto, Portugal, 18–20 July 2018
Ding, Z., Tian, Y.C., Tang, M., Li, Y., Wang, Y.G., Zhou, C.: Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Trans. Industr. Electron. 67(3), 2460–2468 (2020)
Elsayed, S., Sarker, R., Coello Coello, C.A.: Fuzzy rule-based design of evolutionary algorithm for optimization. IEEE Trans. Cybern. 49(1), 301–314 (2019)
Kumar, S., Pandey, M.: Energy aware resource management for cloud data centers. Int. J. Comput. Sci. Inf. Secur. 14(7), 844 (2016)
Liu, Z., Xiang, Y., Qu, X.: Towards optimal CPU frequency and different workload for multi-objective VM allocation. In: 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pp. 367–372 (2015)
Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format+ schema. Google Inc., White Paper, pp. 1–14 (2011)
Sonkiln, C., Tang, M., Tian, Y.C.: A decrease-and-conquer genetic algorithm for energy efficient virtual machine placement in data centers. In: IEEE 15th International Conference on Industrial Informatics (INDIN 2017). Eden, Germany, 24–26 July 2017
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. 24(4), 656–667 (1994)
Wu, G., Tang, M., Tian, Y.-C., Li, W.: Energy-efficient virtual machine placement in data centers by genetic algorithm. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012. LNCS, vol. 7665, pp. 315–323. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34487-9_39
Acknowledgements
This work was supported in part by the Australian Research Council (ARC) through the Discovery Project Scheme under Grant DP220100580 and Grant DP160104292, and the Industrial Transformation Training Centres Scheme under Grant IC190100020.
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Ding, Z. et al. (2024). Accelerated Genetic Algorithm with Population Control for Energy-Aware Virtual Machine Placement in Data Centers. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_2
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DOI: https://doi.org/10.1007/978-981-99-8082-6_2
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