Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm
The resource scheduling strategy of container cloud system plays an important role in resource utilization and cluster performance.The existing container cluster scheduling does not fully take into account the resource occupancy within and between nodes,which is prone to container resource bottlenec...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2023-04-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-233.pdf |
| _version_ | 1797845091755229184 |
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| author | XIE Yongsheng, HUANG Xiangheng, CHEN Ningjiang |
| author_facet | XIE Yongsheng, HUANG Xiangheng, CHEN Ningjiang |
| author_sort | XIE Yongsheng, HUANG Xiangheng, CHEN Ningjiang |
| collection | DOAJ |
| description | The resource scheduling strategy of container cloud system plays an important role in resource utilization and cluster performance.The existing container cluster scheduling does not fully take into account the resource occupancy within and between nodes,which is prone to container resource bottlenecks,resulting in low resource utilization and poor service reliability.In order to balance the workload of container cluster and reduce the bottleneck of container resources,this paper proposes a container cluster scheduling optimization algorithm CS-DQN(container scheduling optimization strategy based on DQN)based on deep Q-lear-ning network(DQN).Firstly,an optimization model of container cluster resource utilization for load balancing is proposed.Then,using the deep reinforcement learning method,a container cluster scheduling algorithm based on DQN is designed,and the relevant state space,action space and reward function are defined.By introducing the improved DQN algorithm,the container dynamic scheduling strategy which meets the optimization goal is generated based on the self-learning method.The prototype experimental results show that the scheduling strategy expands the scale of deployable containers in scheduling,achieves better load balancing in different workloads,improves resource utilization,and the service reliability is better guaranteed. |
| first_indexed | 2024-04-09T17:32:55Z |
| format | Article |
| id | doaj.art-c8416ed5cc65488a9556a3705685351c |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:32:55Z |
| publishDate | 2023-04-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-c8416ed5cc65488a9556a3705685351c2023-04-18T02:33:33ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2023-04-0150423324010.11896/jsjkx.220300215Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN AlgorithmXIE Yongsheng, HUANG Xiangheng, CHEN Ningjiang01 School of Computer and Electronic Information,Guangxi University,Nanning 530004,China ;2 Guangxi Intelligent Digital Services Research Center of Engineering Technology,Nanning 530004,China ;3 Key Laboratory of Parallel, Distributed and Intelligent Computing(Guangxi University), Education Department of Guangxi Zhuang Autonomous Region,Nanning 530004,ChinaThe resource scheduling strategy of container cloud system plays an important role in resource utilization and cluster performance.The existing container cluster scheduling does not fully take into account the resource occupancy within and between nodes,which is prone to container resource bottlenecks,resulting in low resource utilization and poor service reliability.In order to balance the workload of container cluster and reduce the bottleneck of container resources,this paper proposes a container cluster scheduling optimization algorithm CS-DQN(container scheduling optimization strategy based on DQN)based on deep Q-lear-ning network(DQN).Firstly,an optimization model of container cluster resource utilization for load balancing is proposed.Then,using the deep reinforcement learning method,a container cluster scheduling algorithm based on DQN is designed,and the relevant state space,action space and reward function are defined.By introducing the improved DQN algorithm,the container dynamic scheduling strategy which meets the optimization goal is generated based on the self-learning method.The prototype experimental results show that the scheduling strategy expands the scale of deployable containers in scheduling,achieves better load balancing in different workloads,improves resource utilization,and the service reliability is better guaranteed.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-233.pdfcontainer cloud|deep q-learning network|cluster|scheduling strategy |
| spellingShingle | XIE Yongsheng, HUANG Xiangheng, CHEN Ningjiang Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm Jisuanji kexue container cloud|deep q-learning network|cluster|scheduling strategy |
| title | Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm |
| title_full | Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm |
| title_fullStr | Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm |
| title_full_unstemmed | Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm |
| title_short | Self-balanced Scheduling Strategy for Container Cluster Based on Improved DQN Algorithm |
| title_sort | self balanced scheduling strategy for container cluster based on improved dqn algorithm |
| topic | container cloud|deep q-learning network|cluster|scheduling strategy |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-233.pdf |
| work_keys_str_mv | AT xieyongshenghuangxianghengchenningjiang selfbalancedschedulingstrategyforcontainerclusterbasedonimproveddqnalgorithm |