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DBO-DDQN: a cloud-edge collaborative task scheduling strategy integrating dung beetle optimization and temporal difference learning.

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Title: DBO-DDQN: a cloud-edge collaborative task scheduling strategy integrating dung beetle optimization and temporal difference learning.
Authors: Wang, Yu1 (AUTHOR), Tian, Lin2 (AUTHOR), Wang, Chunlin1 (AUTHOR)
Source: Computer Journal. Jun2026, Vol. 69 Issue 6, p1050-1068. 19p.
Subjects: Computer scheduling, Optimization algorithms, Edge computing, Scheduling, Multi-objective optimization, Reinforcement learning
Abstract: To tackle the challenges of scheduling computationally intensive tasks in edge computing environments, this paper proposes a novel hybrid scheduling method called "Dual-Stage Dung Beetle Optimization and Value Approximation Learning Guided by Clustering" (DBO-DDQN). First, a lightweight task clustering method is designed based on task size and time urgency to reduce the computational complexity of task scheduling. Then, the foraging behavior of a dung beetle population is simulated and the foraging process is improved to optimize the matching between task clusters and computing nodes. Finally, a novel temporal difference learning method is proposed to further optimize the matching strategy between tasks within clusters and computing nodes. Experimental results show that, compared to cat swarm optimization (CSO), red-tailed hawk algorithm (RTH), deep Q networks (DQN), DDQN, and Dueling DQN, the proposed algorithm improves task success rates by 15%, 13%, 23%, 19%, and 22%, and enhances weighted multi-objective scheduling performance by 49%, 67%, 33%, 26%, and 22%, respectively, while maintaining low latency and energy consumption. These results highlight the algorithm's effectiveness and superiority in managing large-scale, computation intensive task scheduling within edge computing environments. [ABSTRACT FROM AUTHOR]
Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194728445
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: DBO-DDQN: a cloud-edge collaborative task scheduling strategy integrating dung beetle optimization and temporal difference learning.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yu%22">Wang, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Lin%22">Tian, Lin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Chunlin%22">Wang, Chunlin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. Jun2026, Vol. 69 Issue 6, p1050-1068. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Computer+scheduling%22">Computer scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To tackle the challenges of scheduling computationally intensive tasks in edge computing environments, this paper proposes a novel hybrid scheduling method called "Dual-Stage Dung Beetle Optimization and Value Approximation Learning Guided by Clustering" (DBO-DDQN). First, a lightweight task clustering method is designed based on task size and time urgency to reduce the computational complexity of task scheduling. Then, the foraging behavior of a dung beetle population is simulated and the foraging process is improved to optimize the matching between task clusters and computing nodes. Finally, a novel temporal difference learning method is proposed to further optimize the matching strategy between tasks within clusters and computing nodes. Experimental results show that, compared to cat swarm optimization (CSO), red-tailed hawk algorithm (RTH), deep Q networks (DQN), DDQN, and Dueling DQN, the proposed algorithm improves task success rates by 15%, 13%, 23%, 19%, and 22%, and enhances weighted multi-objective scheduling performance by 49%, 67%, 33%, 26%, and 22%, respectively, while maintaining low latency and energy consumption. These results highlight the algorithm's effectiveness and superiority in managing large-scale, computation intensive task scheduling within edge computing environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1093/comjnl/bxag010
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1050
    Subjects:
      – SubjectFull: Computer scheduling
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Edge computing
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
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      – TitleFull: DBO-DDQN: a cloud-edge collaborative task scheduling strategy integrating dung beetle optimization and temporal difference learning.
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            NameFull: Wang, Yu
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            NameFull: Tian, Lin
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            NameFull: Wang, Chunlin
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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