Enhancing Security in Cloud-Based VM Migration: A Trust-Centric Hybrid Optimization Approach

Abstract

Virtual Machine (VM) migration in cloud computing is essential to accomplishing various resource management goals, such as load balancing, power management, and resource sharing. Ensuring Quality of Service (QoS) is crucial while migrating VMs, which means that VMs must run continuously during the procedure. The dynamic nature of resource requirements in cloud computing, driven by “service-on-demand,” poses security risks, even while live VM migration mechanisms help to maintain VM availability. This paper addresses changing resource requirements and improves overall system security by introducing a novel method for safe live VM migration. In this paper, a robust optimization approach has been proposed for live VM migration named the Gorilla-based Shuffled Shepherd Optimization Approach (GBSSOA). The proposed approach maximizes the number of fitness targets, such as migration time, QoS parameters, and job runtime. The method takes into account trust values in addition to the previously described performance measures, with a focus on security. Key performance indicators QoS as workload, migration time, trust, and GBSSOA-based secure live VM migration are taken into account during the evaluation. The obtained values of 0.141, 0.654, 342.254ms, and 0.569, respectively, show noteworthy accomplishments in the results. These results highlight how well the strategy developed may simultaneously improve performance metrics and strengthen the security of live VM migration in dynamic cloud computing environments.