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

Sai Wang

doi:10.1109/JIOT.2023.3264857

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

2023-09-01

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Sai Wang

龚嶷 (Yi Gong) *

DOI：10.1109/JIOT.2023.3264857

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Mobile-edge computing arises as a promising technology to allow mobile devices (MDs) to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from 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 article. 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 extreme-value descent (EVD) method, the optimal power strategy is obtained. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two subproblems 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 suboptimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.

Keywords:

Edge computing

mixed-integer nonlinear programming (MINLP) problems

power control

task offloading

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

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