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المؤلفYao, Haipeng
المؤلفYuan, Xin
المؤلفZhang, Peiying
المؤلفWang, Jingjing
المؤلفJiang, Chunxiao
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-11-10T09:47:20Z
تاريخ النشر2019
اسم المنشورIEEE Transactions on Vehicular Technology
المصدرScopus
المصدر2-s2.0-85076741722
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TVT.2019.2921792
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36108
الملخصDue to the rapid development of network techniques, packet-switched systems experience high-speed growth of traffic, which imposes a heavy and unbalanced burden on the routers. Hence, efficient routing schemes are required in order to achieve load balance. By decoupling the control plane and the data plane, Software-Defined Network (SDN) shows its flexibility and extensibility to achieve the automatic management of network resources. Based on the SDN architecture, we propose a pair of machine learning aided load balance routing schemes considering the queue utilization (QU), which divide the routing process into three steps, namely the dimension reduction, the QU prediction, as well as the load balance routing. To the best of our knowledge, it is the first time that principal component analysis (PCA) is used for the dimension reduction of the substrate network. Furthermore, QU prediction is conducted with the aid of neural network algorithms for the sake of coping with the network congestion resulting from burst traffic. Finally, simulation results show that our proposed routing schemes considering QU predicted by the machine learning algorithms outperform the traditional Bellman-Ford (BF) routing strategy in terms of the average packet loss ratio, the worst throughput and the average delay. 2019 IEEE.
راعي المشروعManuscript received February 11, 2019; revised April 30, 2019; accepted June 2, 2019. Date of publication June 10, 2019; date of current version August 13, 2019. This research was supported by the Director Foundation Project of National Engineering Laboratory for Public Security Risk Perception and Control by Big Data (PSRPC). The review of this paper was coordinated by Prof. C. Zhang. (Corresponding author: Haipeng Yao.) H. Yao and X. Yuan are with the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: yaohaipeng@bupt.edu.cn; yuanxinbupt@163.com).
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعLoad balance routing
Machine learning
Principal component analysis
Queue utilization
العنوانMachine learning aided load balance routing scheme considering queue utilization
النوعArticle
الصفحات7987-7999
رقم العدد8
رقم المجلد68
dc.accessType Abstract Only


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