Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Year, pages: 2017, 1335 - 1360

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs).

Guo, L., Zhang, Y., Zhao, S. 2017. Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing. In Computing and Informatics, vol. 36, no.6, pp. 1335-1360. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1335

Guo, L., Zhang, Y., Zhao, S. (2017). Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing. Computing and Informatics, 36(6), 1335-1360. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1335