With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This paper studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC’s access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this paper designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this paper designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost.

The proliferation of emerging applications such as augmented reality, face recognition, and autonomous driving have stimulated the growth of demand for low-latency services, while the traditional cloud computing paradigm inevitably increases end-to-end latency. Multi-access edge computing deploys computing and storage resources to user terminals, which is expected to become an effective solution. Most of the existing researches on multi-access edge computing focus on the offloading of independent tasks, which cannot meet the challenge of a real scenario in which a task is composed of multiple interdependent subtasks. To bridge the gap, we formulate the problem of offloading multi-dependent tasks in multi-access edge computing considering both task completion time and execution cost. Since this offloading problem is NP-hard, a queue-based improved multi-objective particle swarm optimization is proposed. During the optimization process, we introduce the Pareto optimal relationship and define a transition probability to obtain the optimal solution. A large number of simulation results show that compared with other alternatives, the performance of our algorithm can be improved by about 3%-25%.