A Cost-Effective and QoS-Aware User Allocation Approach for Edge Computing Enabled IoT

In edge computing, the app vendors hire resources from edge servers and allocate them to app users to overcome the challenge of the limited computing capacities of their IoT devices. An app vendor intends to provide app services to the maximum number of users with the least number of edge servers in order to make efficient use of edge resources while reducing overall system costs. However, when an edge server has to serve more app users than its capacity, the Quality of Service (QoS) deteriorates. Thus, establishing a tradeoff between cost and QoS is a critical challenge in the process of allocating edge computing resources to users. It is referred to as the app user allocation (AUA) problem. To solve the AUA problem, we propose a distributed game-theoretic approach that finds a pure Nash equilibrium (PNE) as the optimal stable solution. We first model the AUA problem as a constrained optimization problem and then introduce a user allocation game (UAGame) to solve it. This UAGame employs a distributed edge server allocation (ESA) algorithm to reach PNE. The time complexity of the ESA algorithm is reduced by the edge server clustering. It has also been shown that the UAGame is a potential game, and therefore the ESA algorithm is guaranteed to converge at PNE. The performance of the ESA algorithm has also been studied theoretically and validated numerically.