ABSTRACT Multi-access edge computing (MEC) has emerged as a promising technique for low-latency services, in light of its proximity to users and embedded cloud computing capability. In order to improve the network efficiency and fairness, it is crucial and challenging to jointly optimize resource management and user association mechanism with the consideration of heterogeneous services and network conditions. Moreover, the existing studies always neglect the heterogeneities of services on the requirements of both computation capability and storage capability. To solve this issue, we derive a strategy to improve the overall delay-aware performance of heterogeneous services with the MEC capability of computation and storage and the choices of the users' association. Accordingly, a coalition-game-based algorithm is proposed to form user coalitions for association scheme and resource sharing policy. In particular, we mathematically present that the proposed algorithm is capable of convergence and optimality. The simulation results also show that our algorithm gets a good performance efficiently. Furthermore, it reduces the weighted sum of delays of users by average 27.8% and 82.1%, while continuously improving delay-aware fairness, compared with those of the priority-based assignment scheme and the nearest assignment scheme, respectively. INDEX TERMS Multi-access edge computing, resource allocation, user association, coalition game.
To address this issue, a game theory approach was proposed in REF , where resource management and user association were optimized in multi-access MEC systems.
Joint Resource Allocation and User Association for Heterogeneous Services in Multi-Access Edge Computing Networks
IEEE Access
IEEE Access
Computer Science