Quality of service optimization algorithm based on deep reinforcement learning in software defined network
Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.A...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2023-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00316/ |
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| _version_ | 1841531115138973696 |
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| author | Cenhuishan LIAO Junyan CHEN Guanping LIANG Xiaolan XIE Xiaoye LU |
| author_facet | Cenhuishan LIAO Junyan CHEN Guanping LIANG Xiaolan XIE Xiaoye LU |
| author_sort | Cenhuishan LIAO |
| collection | DOAJ |
| description | Deep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms. |
| format | Article |
| id | doaj-art-d8d3ea63be664cf392c76f872d8da82f |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-d8d3ea63be664cf392c76f872d8da82f2025-01-15T02:54:38ZzhoChina InfoCom Media Group物联网学报2096-37502023-03-017738259579542Quality of service optimization algorithm based on deep reinforcement learning in software defined networkCenhuishan LIAOJunyan CHENGuanping LIANGXiaolan XIEXiaoye LUDeep reinforcement learning has strong abilities of decision-making and generalization and often applies to the quality of service (QoS) optimization in software defined network (SDN).However, traditional deep reinforcement learning algorithms have problems such as slow convergence and instability.An algorithm of quality of service optimization algorithm of based on deep reinforcement learning (AQSDRL) was proposed to solve the QoS problem of SDN in the data center network (DCN) applications.AQSDRL introduces the softmax deep double deterministic policy gradient (SD3) algorithm for model training, and a SumTree-based prioritized empirical replay mechanism was used to optimize the SD3 algorithm.The samples with more significant temporal-difference error (TD-error) were extracted with higher probability to train the neural network, effectively improving the convergence speed and stability of the algorithm.The experimental results show that the proposed AQSDRL effectively reduces the network transmission delay and improves the load balancing performance of the network than the existing deep reinforcement learning algorithms.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00316/deep reinforcement learningSDNQoSDCNSumTree |
| spellingShingle | Cenhuishan LIAO Junyan CHEN Guanping LIANG Xiaolan XIE Xiaoye LU Quality of service optimization algorithm based on deep reinforcement learning in software defined network 物联网学报 deep reinforcement learning SDN QoS DCN SumTree |
| title | Quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| title_full | Quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| title_fullStr | Quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| title_full_unstemmed | Quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| title_short | Quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| title_sort | quality of service optimization algorithm based on deep reinforcement learning in software defined network |
| topic | deep reinforcement learning SDN QoS DCN SumTree |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00316/ |
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