Optimizing Resource Utilization and QoS-Conscious Application Deployment Through AHP in Edge Computing.

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be in the proximity of IoT users. Thus, the required constraints related to tasks such as resource requirements, and the quality of service can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, 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 while satisfying the Quality of Service (QoS) requirement of IoT users. In any matching algorithm, the main concern is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, and edge server resources used. The simulation results confirm that significant profit and satisfied users can be achieved by the proposed algorithm.