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Multi-layer collaborative task offloading optimization: balancing competition and cooperation across local edge and cloud resources

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Abstract

With the explosive growth of electronic information technology, mobile devices generate massive amounts of data and requirements, which poses a significant challenge to mobile devices with limited computing and battery capacity. Task offloading can transfer computing-intensive tasks from resource-constrained mobile devices to resource-rich servers, thereby significantly reducing the consumption of task execution. How to optimize the task offloading strategy in complex environments with multi-layers and multi-devices to improve efficiency becomes a challenge for the task offloading problem. We optimize the vertical assignment of tasks in a multi-layer system using deep reinforcement learning algorithms, which encompass the cloud, edge, and device layers. To balance the load among multiple devices, we employ the KNN algorithm. Subsequently, we introduce a task state discrimination method based on fuzzy control theory to enhance the performance of computing nodes under high load conditions. By optimizing task offloading policies and execution orders, we successfully reduce the average task execution time and energy consumption of mobile devices. We implemented the proposed algorithm in the PureEdgeSim simulator and performed simulations using different device densities to verify the algorithm’s scalability. The simulation results show that the method we proposed outperforms the methods in previous work. Our method can significantly improve performance in high-device density scenarios.

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Author notes

  1. Xiaoheng Deng and Yuning Huang have contributed equally to this work.

Authors and Affiliations

  1. School of Software, Xinjiang University, Urumqi, 830001, China

    Bowen Ling, Yuning Huang & Yurong Qian

  2. Computer Science and Engineering, Central South University, Changsha, 410083, China

    Xiaoheng Deng, Jingjing Zhang & JinSong Gui

Authors
  1. Bowen Ling
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  2. Xiaoheng Deng
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  3. Yuning Huang
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  4. Jingjing Zhang
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  5. JinSong Gui
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  6. Yurong Qian
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Contributions

Bowen Ling (first author): He was responsible for this research project's overall design and planning and implemented the experimental design, data collection, and preliminary analysis. Meanwhile, Bowen Ling led the paper's writing, including the introduction's first draft, methods, results and discussion. Xiaohang Deng (corresponding author): provided theoretical guidance and technical support for this project, solved critical problems encountered during the research process, and thoroughly reviewed and revised the final paper's content to ensure the study's scientificity and rigour. Other authors made substantial contributions in their respective fields of specialization, including providing research ideas, assisting in experimental operations, participating in discussions, and providing valuable suggestions and improvements to some chapters of the paper.

Corresponding author

Correspondence to Xiaoheng Deng.

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Ling, B., Deng, X., Huang, Y. et al. Multi-layer collaborative task offloading optimization: balancing competition and cooperation across local edge and cloud resources. J Supercomput 80, 26483–26511 (2024). https://doi.org/10.1007/s11227-024-06448-4

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