Enhancing live virtual machine migration process via optimized resource allocation in next generation mobile edge network: A hybrid evolutionary approach

Abstract

SummaryThe evolution of 5th Generation wireless technology introduced Mobile Edge Computing, where edge servers are placed at the edge of the network, and are associated with evolved Node Base Stations (eNBs). This enables mobile users to offload their resource‐intensive tasks to these servers and improve network performance by reducing end‐to‐end delay. However, frequent user mobility leads to frequent re‐planning of network and increases network load. This demands dynamic Virtual Machine (VM) migration in Mobile Edge paradigm for an improved Quality of Service (QoS). For an enhanced VM migration process, an optimal pair of migrating VMs and destination edge servers needs to be chosen. In this paper, we propose an optimized decision‐making policy that chooses such optimal pairs. Several decision parameters such as average wait time, processing delay, migration delay, transmission power, and processing power are modeled. A profit function is developed using these modeled decision parameters that chooses the optimal pairs. This function is maximized using the proposed hybrid evolutionary algorithm, which combines the advantages of PSO and GA. The pairs are chosen in such a manner, that the selection guarantees high network throughput, reduced service delay, and energy consumption which is reflected in the simulation.

A many‐objective optimization based intelligent algorithm for virtual machine migration in mobile edge computing

“…A virtualization framework has been developed for effective management of virtual machines in edge computing. Meanwhile, VMM is applied in various fields, such as cloud federation, 25 cloud computing, 26 and edge computing 27 . However, due to the dynamic nature of the user, the distance the user travels changes the transmission distance of the edge node and thus affects the time, and the cost of communication will increase with the distance traveled.…”

“…Meanwhile, VMM is applied in various fields, such as cloud federation, 25 cloud computing, 26 and edge computing. 27 However, due to the dynamic nature of the user, the distance the user travels changes the transmission distance of the edge node and thus affects the time, and the cost of communication will increase with the distance traveled. In addition, the conflict of interest between users and service providers also affects the overall performance of the network after VMM.…”