Efficient Virtual Machine Selection for Improved Performance in Mobile Edge Computing Environments

Mobile applications have experienced rapid growth thanks to the Internet of Things (IoT). However, the constraints of limited resources on mobile devices (MDs) pose challenges such as processing delays, high energy consumption, and security issues. Mobile Edge Com- puting (MEC) is an effective solution to address these requirements. MEC systems offload tasks from lightweight MDs to Edge servers, where the necessary computations and processing are performed locally. This approach reduces latency, improves the user experience, and saves mobile device resources. MEC systems utilize virtual machines (VMs) sliced into smaller pieces to deliver their services. However, selecting the appropriate VM is a crucial challenge as it impacts overall performance, energy consumption, and user satisfaction. The suitable VM choice must consider the specific requirements of each task, in terms of com- puting capabilities, memory, and other resources. In this work, various approaches and techniques are explored to solve the problem of optimal VM selection in MEC systems. Decision criteria, algorithms, and strate- gies are studied to ensure optimal performance and efficient resource utilization