Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing

作     者：Supreeth, S. Bhargavi, S. Margam, Rakesh Annaiah, H. Nandalike, Rajesh 

作者机构：School of Computer Science and Engineering REVA University Bengaluru India Department of Electronics and Communication Engineering S J C Institute of Technology Chikkaballapur India Healthcare IT Leader SecureKloud Technologies Inc Chicago United States Government Engineering College Karnataka Hassan India Nitte Meenakshi Institute of Technology Bengaluru India 

出 版 物：《SN Computer Science》 (SN COMPUT. SCI.)

年 卷 期：2024年第5卷第1期

页      面：21页

基　　金：REVA 

主　　题：Adam optimizer Physical machines Virtual machine Virtual machine placement White shark optimization 

摘      要：The increasing demand for virtual machine (VM) request is caused due to the increasing number of users. Hence, the VM placement is considered as a critical task for attaining effective resource handling in cloud data centers (DCs). In general, the VM placement procedure deploys the set of VMs onto the set of physical machines (PMs) depending on specific criteria. In this research, the optimal solution for VM placement is computed by hybrid optimization with fitness parameters. Here, the fitness function is computed by combining several objectives including load, power, placement time and migration cost. In addition, VM placement is based on several system factors such as central processing unit (CPU), memory, and bandwidth, million instructions per second (MIPS) and processing elements. Besides, the hybrid optimization technique devised for performing the VM migration in this research is Adam white shark optimization-based VM placement (AWSO_VMP), which is formulated by modifying the white shark optimization (WSO) with the Adam optimizer. Thus, the performance of AWSO_VMP is assessed using load, power consumption and cost of migration, and the attained values of corresponding metrics are 0.133, 0.225 W and 0.116. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.