Energy-efficient multiuser and multitask computation offloading optimization method  

作　　者：Meini Pan Zhihua Li Junhao Qian 

机构地区：[1]School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China [2]School of IoT Engineering,Jiangnan University,Wuxi 214122,China

出　　处：《Intelligent and Converged Networks》2023年第1期76-92,共17页智能与融合网络（英文）

基　　金：supported by the Smart Manufacturing New Model Application Project Ministry of Industry and Information Technology(No.ZH-XZ-18004);the Future Research Projects Funds for the Science and Technology Department of Jiangsu Province(No.BY2013015-23);the Fundamental Research Funds for the Ministry of Education(No.JUSRP211A 41);the Fundamental Research Funds for the Central Universities(No.JUSRP42003);the 111 Project(No.B2018).

摘　　要：For dynamic application scenarios of Mobile Edge Computing(MEC),an Energy-efficient Multiuser and Multitask Computation Offloading(EMMCO)optimization method is proposed.Under the consideration of multiuser and multitask computation offloading,first,the EMMCO method takes into account the existence of dependencies among different tasks within an implementation,abstracts these dependencies as a Directed Acyclic Graph(DAG),and models the computation offloading problem as a Markov decision process.Subsequently,the task embedding sequence in the DAG is fed to the RNN encoder-decoder neural network with combination of the attention mechanism,the long-term dependencies among different tasks are successfully captured by this scheme.Finally,the Improved Policy Loss Clip-based PPO2(IPLC-PPO2)algorithm is developed,and the RNN encoder-decoder neural network is trained by the developed algorithm.The loss function in the IPLC-PPO2 algorithm is utilized as a preference for the training process,and the neural network parameters are continuously updated to select the optimal offloading scheduling decisions.Simulation results demonstrate that the proposed EMMCO method can achieve lower latency,reduce energy consumption,and obtain a significant improvement in the Quality of Service(QoS)than the compared algorithms under different situations of mobile edge network.

关 键 词：Mobile Edge Computing(MEC) computation offloading Reinforcement Learning(RL) optimization model 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]