An Energy and Latency Trade-off for Resources Allocation in a MEC System

Abstract

This paper addresses the issue of efficient resource allocation in a Mobile Edge Computing (MEC) system, taking into account the trade-off between energy consumption and operation latency. The increasing deployment of connected devices and data-intensive services in the Internet of Things (IoT) poses significant challenges in terms of managing computational resources. In this study, we propose a MEC system model that considers energy constraints and the need to minimize latency to ensure optimal performance. We formulate the resource allocation problem in terms of a trade-off between energy consumption and latency, and explore solutions based on heuristic task offloading techniques. Our experiments demonstrate that our approach achieves improved latency performance while reducing energy consumption. We also evaluate the impact of various parameters, such as workload and resource availability, on the energy-latency trade-off.

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

“…Offloading computations to edge servers can significantly enhance the performance of mobile applications by leveraging the proximity of computational resources and reducing the communication latency between devices and servers [4]. However, the seamless integration of computation offloading into MEC networks presents a myriad of challenges, particularly concerning security, privacy, and resource management [5], [7].…”