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Multi-objective computation offloading based on Invasive Tumor Growth Optimization for collaborative edge-cloud computing

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Abstract

The rapid development of 5G network-connected Internet of Things (IoT) has attracted great attention of academia and industry with a huge demand for IoT application task processing, which is usually delay-sensitive with the constraints of resource limitation and cost budget. Facing the challenges of massive computing tasks, various users’ requirements and resource limitations, this paper formulates a multi-objective optimization problem for computation offloading in a collaborative edge-cloud computing paradigm for handling various types of IoT tasks, aiming to provide efficient computing service to multiple IoT users. With the aim of minimizing task processing delay, mobile device energy consumption and economic cost, a discrete multi-objective Invasive Tumor Growth Optimization algorithm based on Invasive Tumor Growth Optimization is proposed to obtain diverse Pareto-optimal solutions, employing four types of cells with different search strategies to improve the search efficiency, convergence and diversity. Simulation results under complex scenarios show that the proposed algorithm can effectively solve the modeled computation offloading optimization problem of various task types and task scales and can also be well applied to a variety of different computing platforms. Compared with state-of-the-art algorithms, our proposed algorithm achieves better convergence and diversity in three objectives, which implies superior performance, scalability and applicability.

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Funding

This research was funded by National Natural Science Foundation of China (61976239), Innovation Foundation of High-end Scientific Research Institutions in Zhongshan of China (2019AG031), and Natural Science Foundation of Guangdong Province of China (2021A1515011942).

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Authors and Affiliations

  1. Communication and Computer Network Lab of GD, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510000, Guangdong, China

    Xiaofei Wu, Shoubin Dong, Jinlong Hu & Qianxue Hu

Authors
  1. Xiaofei Wu
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  2. Shoubin Dong
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  3. Jinlong Hu
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  4. Qianxue Hu
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Contributions

XW contributed to methodology, software, formal analysis, writing—original draft, and visualization. SD contributed to resources, writing—review and editing, supervision, and project administration. JH performed investigation and writing—review and editing. QH contributed to validation and software.

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Correspondence to Shoubin Dong.

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Wu, X., Dong, S., Hu, J. et al. Multi-objective computation offloading based on Invasive Tumor Growth Optimization for collaborative edge-cloud computing. Soft Comput 27, 17747–17761 (2023). https://doi.org/10.1007/s00500-023-09051-6

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