A Joint Distributed Computation Offloading and Resource Allocation Scheme for MEC Networks

Multi-access edge computing (MEC) technology is a potential solution to the conflict between resource-constrained unmanned aerial vehicles (UAVs) and various emerging computationally intensive, delay-sensitive applications of UAVs. In this paper, we study the joint optimization problem of distributed computation offloading and resource allocation in MEC-supported UAV application scenarios, where the task of each UAV can be divided and offloaded to multiple servers deployed in ground wireless access points (WAPs) for parallel processing. To enhance the quality of user experience (QoE) while avoiding excessive optimization, we propose a new user satisfaction function to evaluate the task processing level of MEC network, which respectively scores the actual task processing delay and energy consumption based on different criteria determined by task requirements. We aim to trade off delay and energy consumption by jointly optimizing server selection, computing resource allocation, transmission power control, and task allocation to achieve maximum user satisfaction. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. To reduce the complexity of the joint optimization problem, it is decomposed into three sub-problems based on the divide-and-conquer approach: server computing resource allocation, transmission power control, and task offloading decision. We propose a three-layer intelligent optimization (TIO) algorithm, which employs the Lagrange multiplier method, the AdaDelta gradient descent method, and our proposed multivariate decoupling-particle swarm optimization (MD-PSO) algorithm to solve the above three sub-problems alternately for gradually approaching the maximum user satisfaction. The numerical results show that our proposed algorithm is significantly superior to the benchmark algorithms in terms of solving speed, accuracy, and stability.