Joint Power Control and Task Offloading in Collaborative Edge–Cloud Computing Networks

Abstract

Mobile edge computing arises as a promising technology to allow mobile devices to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from mobile devices (MDs) lead to a large latency due to limited computing and channel resources. To mitigate this situation, a collaborative edge-cloud computing network is considered in this paper. Based on power control and task offloading, we formulate a mixed-integer nonlinear programming (MINLP) problem to minimize the weighted sum of the energy consumption and the latency. This NP-hard problem is decomposed into a real variable problem and an integer linear programming problem. By leveraging the proposed extremevalue descent (EVD) method, the optimal power strategy is obtained by solving the real variable problem. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two sub-problems are well solved, the solutions may be unsatisfactory for the original problem. To find a high-quality solution, we use an alternating optimization (AO) method to jointly optimize the decomposed problems. Theoretical analysis demonstrates that the EVD method converges at the rate of O(1/s) and the AO method can converge to a sub-optimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.