A method for virtual machine migration in cloud computing using a collective behavior-based metaheuristics algorithm

作     者：Sha, Jing Ebadi, Abdol Ghaffar Mavaluru, Dinesh Alshehri, Mohmmed Alfarraj, Osama Rajabion, Lila 

作者机构：Shandong Univ Sci & Technol Sch Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Islamic Azad Univ Dept Agr Jouybar Branch Jouybar Iran Saudi Elect Univ Coll Comp & Informat Dept Informat Technol Riyadh Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Al Majmaah Saudi Arabia King Saud Univ Community Coll Comp Sci Dept Riyadh Saudi Arabia Univ South Florida Sarasota Manatee Coll Business Sarasota FL USA 

出 版 物：《CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE》 (并行学和计算：实践与经验)

年 卷 期：2020年第32卷第2期

页      面：e5441-e5441页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Deanship of Scientific Research at Majmaah University [RGP-2019-26] 

主　　题：bacteria foraging optimization algorithm cloud computing energy consumption migration virtual machine virtualization 

摘      要：Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources energy, energy management is also very important and challenging. Due to the NP-hard nature of this problem, this paper presents an energy-aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior-based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.