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Intelligent and Converged Networks 2023, 4(1): 76-92 https://doi.org/10.23919/ICN.2023.0007
Open Access | Issue | Published: 20 March 2023

Energy-efficient multiuser and multitask computation offloading optimization method

Show Author's Information Meini Pan1,  Zhihua Li1,  Junhao Qian2( )
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
School of IoT Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
Mobile Edge Computing (MEC), computation offloading, optimization model, Reinforcement Learning (RL)
Cite this article:
Pan M, Li Z, Qian J. Energy-efficient multiuser and multitask computation offloading optimization method. Intelligent and Converged Networks, 2023, 4(1): 76-92. https://doi.org/10.23919/ICN.2023.0007
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Abstract Full text About this article

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.


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About this article

Energy-efficient multiuser and multitask computation offloading optimization method

Show Author's information Meini Pan1, Zhihua Li1, Junhao Qian2( )
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
School of IoT Engineering, Jiangnan University, Wuxi 214122, China

Abstract

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.

Keywords: Mobile Edge Computing (MEC), computation offloading, optimization model, Reinforcement Learning (RL)

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Publication history

Received: 31 March 2023
Accepted: 20 April 2023
Published: 20 March 2023
Issue date: March 2023

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© All articles included in the journal are copyrighted to the ITU and TUP.

Acknowledgements

Acknowledgment

This work was 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), and the 111 Project (No. B2018).

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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