Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing

• URL: http://arxiv.org/abs/2001.06585v2
• Date: Thu, 25 Jun 2020 07:03:53 GMT
• Title: Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing
• Authors: Zhengping Liang, Jian Zhang, Liang Feng, Zexuan Zhu
• Abstract summary: Evolutionary algorithms (EAs) have been performed promising-solving on virtual machine placement (VMP) problems in the past. As growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement problem. This paper aims to apply the MFO technology to the LVMP problem in heterogeneous environment.
• Score: 15.840835256016259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The placement scheme of virtual machines (VMs) to physical servers (PSs) is crucial to lowering operational cost for cloud providers. Evolutionary algorithms (EAs) have been performed promising-solving on virtual machine placement (VMP) problems in the past. However, as growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement (LVMP) problem due to the high time complexity and poor scalability. Recently, the multi-factorial optimization (MFO) technology has surfaced as a new search paradigm in evolutionary computing. It offers the ability to evolve multiple optimization tasks simultaneously during the evolutionary process. This paper aims to apply the MFO technology to the LVMP problem in heterogeneous environment. Firstly, we formulate a deployment cost based VMP problem in the form of the MFO problem. Then, a multi-factorial evolutionary algorithm (MFEA) embedded with greedy-based allocation operator is developed to address the established MFO problem. After that, a re-migration and merge operator is designed to offer the integrated solution of the LVMP problem from the solutions of MFO problem. To assess the effectiveness of our proposed method, the simulation experiments are carried on large-scale and extra large-scale VMs test data sets. The results show that compared with various heuristic methods, our method could shorten optimization time significantly and offer a competitive placement solution for the LVMP problem in heterogeneous environment.

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