QoS-aware resource allocation in mobile edge computing networks: Using intelligent offloading and caching strategy

Mobile edge computing (MEC) is a key feature of next generation heterogeneous networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. In this research we investigated on connection management in multi-access edge computing systems using intelligent offloading and caching strategy. This paper presents joint QoS-aware resource allocation and mobile edge computing in multi-access heterogeneous networks in order to maximize total system’s energy efficiency in addition to guaranty the users QoS requirements. Firstly, based on the multi-server MEC scenario, a new goal function is designed considering calculation and communication models, in order to decreasing the completion time of all computing tasks and achieving optimal energy efficiency under delay constraints. Then, the continues carrier allocation and user association variables in addition to the interference coordination incorporated in the goal function, modifies the primary optimization problem to a mixed integer nonlinear programming (MINLP). Also, considering user’s minimum data rate and maximum transmission power constraints, a carrier-matching algorithm is introduced to obtain the optimal channel allocation strategy, which first matches the user with the channel and subsequently, the Dinkelbach-like method is applied to obtain the optimal resource allocation. Based on the simulations, the proposed approach not only achieves higher energy efficiency but also enhances the total network throughput in multiple-sources scenarios.