Latency-Aware Joint Task Offloading and Energy Control for Cooperative Mobile Edge Computing

In the application of the Internet of Things (IoT), existing cloud edge collaboration technologies face the problem of poor coordination of heterogeneous resources. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CFEMC</i>, which is a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>loud-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i>og-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i>dge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>ulti-layer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>ollaboration resource scheduling framework for IoT. First, we design a collaborative resource scheduling framework based on semi-distributed artificial intelligence. It can achieve collaborative optimization of cloud/edge computing resource allocation under the constraints of high reliability and low latency. Second, we present a workflow applications scheduling strategy based on the proposed collaborative resource scheduling framework. This can solve the problem of unstable computing performance and transmission bandwidth during the scheduling process. Finally, the extensive and real data supported simulation results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CFEMC</i> has advantages in terms of energy consumption, delay and throughput compared with other benchmark strategies. Against CEC Hu et al. 2023 and PSO Zeng et al. 2022, the average throughput increases by 16.37% and 24.21%, and the total queuing delay decreases by 54.23% and 58.12%, respectively.