QoS-Aware Application Assignment and Resource Utilization Maximization Using AHP in Edge Computing

Abstract

Edge computing (EC) has emerged as a promising technology to meet the demand for computational resources in Internet of Things (IoT) networks. With EC, the processing of massive data-intensive tasks can occur in proximity to IoT users. Thus, required constraints related to tasks, such as latency and Quality of Service (QoS) can be guaranteed. However, determining the task offloading strategy under various constraints, including resources, distance, and cost, remains an open issue. In this article, we study the task offloading problem from a matching perspective and propose an edge-user assignment algorithm (EUAA) that aims to maximize the resource utilization of edge servers and the number of assigned IoT users. A key concern in any matching algorithm is how to generate the preference order for either side. To generate preference orders for edge servers, we apply the analytical hierarchy process (AHP), considering criteria, such as distance from users to the server, latency, resource requirements, and pricing. This approach establishes the priority of users for matching to edge servers. From the IoT users’ perspective, we use cost and QoS parameters to enhance their satisfaction. We evaluate the performance of the proposed model based on the number of assigned users, server profit, number of satisfied users, edge server resource utilization, and execution time, comparing it with state-of-the-art schemes.