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.

5G networks promote integrating Multi-access Edge Computing (MEC) capabilities in Dense Small Cell (DSC) networks in order to address the strict latency requirement of Smart Mobile Devices (SMDs) services. This article investigates the contribution of a split-cache method integrated within a secure blockchain based MEC system and present a novel method to enhance service provision by distributing the containers’ base image chunks among multiple Edge-Cloud Nodes (ECNs). The objective of this study is to minimize the service starting/restarting time by collecting, verifying and compiling the best chunks candidates while resources allocation is considered at each request slot. The formulated problem is a mixed-integer nonlinear program, subject to network bandwidth capacity, base image chunks availability, and services starting deadlines constraint. Finally, the numerical results of the proposed two solutions demonstrate its benefits regarding service provision operations while taking bandwidth,timing and resources into consideration.