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Open AccessArticle
Container Scheduling Algorithms for Distributed Cloud Environments
by
Honghua Chen
Honghua Chen
HongHua Chen was born in Zhejiang, China, in 1998. She received her B.S. degree in information from [...]
HongHua Chen was born in Zhejiang, China, in 1998. She received her B.S. degree in information security from the College of Cryptography Engineering, Engineering University of People's Armed Police, Xi'an, China, in 2022, where she is currently pursuing her M.S. degree. Her research interests include cloud computing, artificial intelligence, and resource scheduling.
1,*,†
,
Cong Shen
Cong Shen 2,†
,
Xinyuan Qiu
Xinyuan Qiu 1 and
Chuanqi Cheng
Chuanqi Cheng 3
1
College of Information Engineering, Engineering University of PAP, Xi’an 710086, China
2
College of Cryptography Engineering, Engineering University of PAP, Xi’an 710086, China
3
North Cloud Key Laboratory, Engineering University of PAP, Xi’an 710086, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Processes 2024, 12(9), 1804; https://doi.org/10.3390/pr12091804 (registering DOI)
Submission received: 5 August 2024
/
Revised: 21 August 2024
/
Accepted: 22 August 2024
/
Published: 25 August 2024
Abstract
Due to the difficulty of existing container scheduling algorithms to adapt to large-scale complex scenarios and meet the diverse application and load requirements, this study delves into a groundbreaking hybrid scheduling approach that melds Deep Deterministic Policy Gradient (DDPG) with a Genetic Algorithm (GA). The proposed method initially employs a container grouping policy to reduce the overhead associated with frequent inter-container calls. Subsequently, to address the computational inefficiency of large-scale scheduling, a genetic algorithm is utilized for rapid global optimization. To overcome the problem of genetic algorithms being highly susceptible to falling into local optimality, the DDPG algorithm is applied for local optimization, with a cross-mutation operation introduced to escape local optima. The experimental outcomes demonstrate that the proposed algorithm enhances cluster load balancing by 81.13%, improves the fitness function by 3.26%, reduces completion time by 19.06%, and decreases container dependency overhead by 2.75%. Furthermore, under the experimental conditions, the system performs best when the group size is 10. This research offers a novel paradigm for the development of container scheduling algorithms in distributed intelligent data clouds, advancing the field of resource management in cloud computing environments.
Share and Cite
MDPI and ACS Style
Chen, H.; Shen, C.; Qiu, X.; Cheng, C.
Container Scheduling Algorithms for Distributed Cloud Environments. Processes 2024, 12, 1804.
https://doi.org/10.3390/pr12091804
AMA Style
Chen H, Shen C, Qiu X, Cheng C.
Container Scheduling Algorithms for Distributed Cloud Environments. Processes. 2024; 12(9):1804.
https://doi.org/10.3390/pr12091804
Chicago/Turabian Style
Chen, Honghua, Cong Shen, Xinyuan Qiu, and Chuanqi Cheng.
2024. "Container Scheduling Algorithms for Distributed Cloud Environments" Processes 12, no. 9: 1804.
https://doi.org/10.3390/pr12091804
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