A Low-Cost Energy-Efficient Optimized Hybrid BFPHM in Virtual Machine Migration on Cloud Data Centres

Abstract

In view of recent advancements in cloud technology and the increasing workload of consumers, there has been a noticeable decrease in the quality of service (QoS) being provided. In order to achieve enhanced cloud computing services, which can effectively cater to a large number of cloud consumers while optimising both time and energy consumption in cloud data centres (CDCs), it is essential to use an efficient virtual machine migration technique. In previous studies on the relocation of virtual machines (VMs) between two physical hosts, it was observed that the process involves several steps. Firstly, the VM needs to be shut down in order to initiate the migration. Subsequently, the necessary resources and services are allocated to the new host to accommodate the VM. Following this, the virtual machine records are transferred to the new host. Finally, the virtual machine is started on the new host to complete the migration process. The transmission of a virtual machine (VM) includes the transfer of its phase, which covers aspects such as storage, internal devices, and the processing system of the VM. In this proposed study, we aim to develop a cost-effective and energy-efficient approach for virtual machine (VM) migration, which we refer to as the Bacterial Foraging Prioritised Hybrid Model VMM (BFPHM). The proposed methodology aims to optimize the selection of relocation-capable hosts within networks, resulting in improved optimization outcomes. This paper proposes an efficient-cost and energy-effective methodology using the Bacterial Foraging Prioritized Hybrid Model for VMM. The model of BFPHM effectively identifies overloaded hosts and optimally selects suitable hosts to migrate their VMs, which in turn gives better optimization results. Comparative performance analysis shows that BFPHM outperforms experience-based migration techniques from 2019, performing better in terms of energy efficiency with lower migration counts and assuring proper service quality compared to GHO.