A Resource Allocation Strategy to Enhance User Experience for IoT Devices in Multi-access Edge Computing

Abstract

Multi-access Edge Computing (MEC) has become a key paradigm in contemporary network topologies due to the spread of Internet of Things (IoT) devices and the growing need for low-latency, high-performance applications. Reducing latency and enhancing performance, MEC places processing resources closer to end users. IoT applications have a variety of changing needs, therefore effectively allocating these few resources to meet those needs continues to be a major difficulty. This paper addresses this challenge by proposing a novel greedy resource allocation strategy designed to optimize the user ex-perience of IoT devices in MEC environments. Our approach aims to optimally distribute computational resources to meet the requirements of IoT applications while maintaining a high Quality of Experience (QoE). We propose a greedy algorithm that dynamically allocates resources based on real-time user demands, server computational capabilities, processing delay, and application characteristics. Extensive simulations have demon-strated that our strategy significantly improves key performance indicators such as response time, task failure rate, average QoE, and computational resource utilization rate compared to traditional allocation methods. This research provides a scalable and effective solution to the challenges associated with IoT in MEC, paving the way for more responsive and reliable edge computing systems.