Joint Power Control and Resource Allocation With Task Offloading for Collaborative Device-Edge-Cloud Computing Systems

Collaborative edge and cloud computing is a promising computing paradigm for reducing the task response delay and energy consumption of devices. In this paper, we aim to jointly optimize task offloading strategy, power control for devices, and resource allocation for edge servers within a collaborative device-edge-cloud computing system. We formulate this problem as a constrained multiobjective optimization problem and propose a joint optimization algorithm (JO-DEC) based on a multiobjective evolutionary algorithm to solve it. To address the tight coupling of the variables and the high-dimensional decision space, we propose a decoupling encoding strategy (DES) and a boundary point sampling strategy (BPS) to improve the performance of the algorithm. The DES is utilized to decouple the correlations among decision variables, and BPS is employed to enhance the convergence speed and population diversity of the algorithm. Simulation results demonstrate that JO-DEC outperforms three state-of-the-art algorithms in terms of convergence and diversity, enabling it to achieve a smaller task response delay and lower energy consumption.