Title: A data transmission approach with energy reduction based on virtual machine migration technique in cloud computing

Authors: H. Anwar Basha; R. Saravanakumar; K. Prabu; Divyendu Kumar Mishra; S. Narayanan; A. Samydurai

Addresses: School of Computer Science and Engineering, Reva University, Bengaluru, India ' Department of Wireless Communication, Institute of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nādu, India ' PG and Research Department of Computer Science, Sudharsan College of Arts and Science, Pudukkottai, Tamil Nadu, India ' Department of Computer Science and Engineering, Faculty of Engineering, VBS Purvanchal University, Jaunpur, Uttar Pradesh, India ' Department of Information Technology, SRM Valliammai Engineering College, Tamil Nadu, India ' Department of Computer Science and Engineering, SRM Valliammai Engineering College, Tamil Nadu, India

Abstract: To provide quicker data access, database centres use virtual machines (VMs) migration to maintain regular content pages in the necessary unit. Memory sharing without downtime is ideal for offline VM migration. However, it has several problems while migrating active VMs. To improve bandwidth availability and hardware stability, it is utilised in workload balancing, low energy retains, and dynamic VM resizing. Thus, needless memory (dirty pages) moving leads in lengthy migration time and downtime. To minimise energy usage and the number of VM migration stages, we offer the NPA-FLI-EC. It combines neural prior prediction algorithm and fuzzy logic insertion of energy reduction on VMM method. Using NPA-FLI-EC, it may optimise VM placement and minimise connection loss on physical servers, while anticipating resource identification from each host reduces needless VM migrations. Thus, it allows for task diversification over multiple servers while saving 2/3 of total energy usage. It also saves bandwidth and improves energy efficiency by consolidating the number of VMs.

Keywords: virtual machine; VM migration; data sharing; evolutionary computing.

DOI: 10.1504/IJESMS.2023.127393

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.1, pp.43 - 51

Received: 31 Jul 2021
Accepted: 02 Sep 2021
Published online: 03 Dec 2022 *