{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:58:17Z","timestamp":1774533497425,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s11276-021-02883-w","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T09:02:56Z","timestamp":1645088576000},"page":"4133-4144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep reinforcement learning based multi-layered traffic scheduling scheme in data center networks"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5714-6252","authenticated-orcid":false,"given":"Guihua","family":"Wu","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"2883_CR1","doi-asserted-by":"publisher","first-page":"178526","DOI":"10.1109\/ACCESS.2020.3027675","volume":"8","author":"TZ Emara","year":"2020","unstructured":"Emara, T. Z., & Huang, J. Z. (2020). Distributed data strategies to support large-scale data analysis across geo-distributed data centers. IEEE ACCESS, 8, 178526\u2013178538.","journal-title":"IEEE ACCESS"},{"key":"2883_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.comcom.2017.03.009","volume":"107","author":"A Pag\u00e8s","year":"2017","unstructured":"Pag\u00e8s, A., Serrano, R., Perell\u00f3, J., & Spadaro, S. (2017). On the benefits of resource disaggregation for virtual data centre provisioning in optical data centres. Computer Communications, 107, 60\u201374.","journal-title":"Computer Communications"},{"key":"2883_CR3","doi-asserted-by":"publisher","first-page":"90630","DOI":"10.1109\/ACCESS.2020.2994328","volume":"8","author":"Z Liao","year":"2020","unstructured":"Liao, Z., Peng, J., Chen, Y., Zhang, J., & Wang, J. (2020). A fast Q-learning based data storage optimization for low latency in data center networks. IEEE ACCESS, 8, 90630\u201390639.","journal-title":"IEEE ACCESS"},{"key":"2883_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6337","author":"S Nasirian","year":"2021","unstructured":"Nasirian, S., Faghani, F., Farzanegan, M. D. (2021). Doughnutie: An efficient and low-latency cloud data center network architecture. Concurrency and Computation-Practice &amp; Experience. https:\/\/doi.org\/10.1002\/cpe.6337","journal-title":"Concurrency and Computation-Practice & Experience"},{"issue":"4","key":"2883_CR5","first-page":"1079","volume":"20","author":"R Ranjana","year":"2019","unstructured":"Ranjana, R., Radha, S., & Raja, J. (2019). A novel approach to adaptive flow scheduling for energy efficient data center network. Journal of Internet Technology, 20(4), 1079\u20131086.","journal-title":"Journal of Internet Technology"},{"issue":"8","key":"2883_CR6","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1109\/TPDS.2017.2666807","volume":"28","author":"M Adda","year":"2017","unstructured":"Adda, M., & Peratikou, A. (2017). Routing and fault tolerance in Z-fat tree. IEEE Transactions on Parallel and Distributed Systems, 28(8), 2373\u20132386.","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"issue":"4","key":"2883_CR7","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1145\/2377677.2377698","volume":"42","author":"N Farrington","year":"2012","unstructured":"Farrington, N., Porter, G., Sun, P.-C., Forencich, A., Ford, J., Fainman, Y., Papen, G., & Vahdat, A. (2012). A demonstration of ultra-low-latency data center optical circuit switching. ACM SIGCOMM Computer Communication Review, 42(4), 95\u201396.","journal-title":"ACM SIGCOMM Computer Communication Review"},{"key":"2883_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/en12142757","author":"F Wang","year":"2019","unstructured":"Wang, F., Huang, Y., & Prasetyo, B. (2019). Energy-efficient improvement approaches through numerical simulation and field measurement for a data center. Energies. https:\/\/doi.org\/10.3390\/en12142757","journal-title":"Energies"},{"issue":"8","key":"2883_CR9","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1109\/TC.2014.2357810","volume":"64","author":"X Zhan","year":"2015","unstructured":"Zhan, X., & Reda, S. (2015). Power budgeting techniques for data centers. IEEE Transactions on Computers, 64(8), 2267\u20132278.","journal-title":"IEEE Transactions on Computers"},{"issue":"5","key":"2883_CR10","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1142\/S0129183102003450","volume":"13","author":"M Andrecut","year":"2002","unstructured":"Andrecut, M., & Ali, M. K. (2002). Fuzzy reinforcement learning. International Journal of Modern Physics C, 13(5), 659\u2013674.","journal-title":"International Journal of Modern Physics C"},{"key":"2883_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.robot.2019.01.003","volume":"114","author":"C You","year":"2019","unstructured":"You, C., Jianbo, L., Filev, D., & Tsiotras, P. (2019). Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning. Robotics and Autonomous Systems, 114, 1\u201318.","journal-title":"Robotics and Autonomous Systems"},{"key":"2883_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/s20030939","author":"C Wang","year":"2020","unstructured":"Wang, C., Zhang, Q., Tian, Q., Li, S., Wang, X., Lane, D., Petillot, Y., & Wang, Sen. (2020). Learning mobile manipulation through deep reinforcement learning. Sensors. https:\/\/doi.org\/10.3390\/s20030939","journal-title":"Sensors"},{"issue":"2","key":"2883_CR13","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1109\/COMST.2015.2402617","volume":"17","author":"DF Macedo","year":"2015","unstructured":"Macedo, D. F., Guedes, D., Vieira, L. F. M., Vieira, M. A. M., & Nogueira, M. (2015). Programmable networks-from software-defined radio to software-defined networking. IEEE Communications Surveys and Tutorials, 17(2), 1102\u20131125.","journal-title":"IEEE Communications Surveys and Tutorials"},{"issue":"4","key":"2883_CR14","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1093\/imamci\/dnaa011","volume":"37","author":"L Lu","year":"2020","unstructured":"Lu, L. (2020). Multi-path allocation scheduling optimization algorithm for network data traffic based on SDN architecture. IMA Journal of Mathematical Control and Information, 37(4), 1237\u20131247.","journal-title":"IMA Journal of Mathematical Control and Information"},{"issue":"2","key":"2883_CR15","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s12083-017-0623-z","volume":"12","author":"J Xiao","year":"2019","unstructured":"Xiao, J., Chen, S., & Sui, M. (2019). The strategy of path determination and traffic scheduling in private campus networks based on SDN. Peer-to-Peer Networking and Applications, 12(2), 430\u2013439.","journal-title":"Peer-to-Peer Networking and Applications"},{"key":"2883_CR16","doi-asserted-by":"publisher","unstructured":"Aroua, S., Quadrio, G., Ghamri-Doudane, Y., Gaggi, O., & Palazzi, C.E. (2020). QoS-aware reinforcement learning for multimedia traffic scheduling in home area networks, 2020 IEEE Global Communications Conference (GLOBECOM), 2020. https:\/\/doi.org\/10.1109\/GLOBECOM42002.2020.9348035.","DOI":"10.1109\/GLOBECOM42002.2020.9348035"},{"issue":"5","key":"2883_CR17","doi-asserted-by":"publisher","first-page":"974","DOI":"10.21629\/JSEE.2019.05.14","volume":"30","author":"X Xu","year":"2019","unstructured":"Xu, X., Chen, Y., Hu, L., Kumar, A. (2019). MTSS: Multi-path traffic scheduling mechanism based on SDN. Journal of Systems Engineering and Electronics, 30(5), 974\u2013984.","journal-title":"Journal of Systems Engineering and Electronics"},{"issue":"6","key":"2883_CR18","doi-asserted-by":"publisher","first-page":"5240","DOI":"10.1109\/JIOT.2018.2872439","volume":"5","author":"C Lin","year":"2018","unstructured":"Lin, C., Bi, Y., Zhao, H., Liu, Z., Jia, S., & Zhu, J. (2018). DTE-SDN: A dynamic traffic engineering engine for delay-sensitive transfer. IEEE Internet of Things Journal, 5(6), 5240\u20135253.","journal-title":"IEEE Internet of Things Journal"},{"issue":"5","key":"2883_CR19","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1007\/s12652-018-0783-6","volume":"10","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Tang, F., & Barolli, L. (2019). Efficient flow detection and scheduling for SDN-based big data centers. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1915\u20131926.","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"issue":"3","key":"2883_CR20","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/TCOMM.2020.3042271","volume":"69","author":"I Maity","year":"2021","unstructured":"Maity, I., Misra, S., & Mandal, C. (2021). DART: Data plane load reduction for traffic flow migration in SDN. IEEE Transactions on Communications, 69(3), 1765\u20131774.","journal-title":"IEEE Transactions on Communications"},{"issue":"1","key":"2883_CR21","doi-asserted-by":"publisher","first-page":"247","DOI":"10.3233\/JIFS-179399","volume":"38","author":"H Li","year":"2020","unstructured":"Li, H., Lu, H., & Fu, X. (2020). An optimal and dynamic elephant flow scheduling for SDN-based data center networks. Journal of Intelligent &amp; Fuzzy Systems, 38(1), 247\u2013255.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"2883_CR22","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8825643","author":"H Li","year":"2020","unstructured":"Li, H., & Chen, X. (2020). DRL-based edge computing model to offload the FIFA world cup traffic. Mobile Information Systems. https:\/\/doi.org\/10.1155\/2020\/8825643","journal-title":"Mobile Information Systems"},{"issue":"5","key":"2883_CR23","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1007\/s10766-021-00711-4","volume":"49","author":"T Chen","year":"2021","unstructured":"Chen, T., Li, W., Sun, Y., & Li, Y. (2021). M-DRL: Deep reinforcement learning based coflow traffic scheduler with MLFQ threshold adaption. International Journal of Parallel Programming, 49(5), 646\u2013657.","journal-title":"International Journal of Parallel Programming"},{"key":"2883_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.107891","author":"P Sun","year":"2021","unstructured":"Sun, P., Guo, Z., Lan, J., Li, J., Yuxiang, H., & Baker, T. (2021). ScaleDRL: A scalable deep reinforcement learning approach for traffic engineering in SDN with pinning control. Computer Networks. https:\/\/doi.org\/10.1016\/j.comnet.2021.107891","journal-title":"Computer Networks"},{"key":"2883_CR25","doi-asserted-by":"publisher","first-page":"129955","DOI":"10.1109\/ACCESS.2019.2940445","volume":"7","author":"Y Tang","year":"2019","unstructured":"Tang, Y., Guo, H., Yuan, T., Gao, X., Hong, X., Li, Y., Qiu, J., Zuo, Y., & Jian, W. (2019). Flow splitter: A deep reinforcement learning-based flow scheduler for hybrid optical-electrical data center network. IEEE Access, 7, 129955\u2013129965.","journal-title":"IEEE Access"},{"issue":"2","key":"2883_CR26","first-page":"279","volume":"38","author":"L Gu","year":"2020","unstructured":"Gu, L., Zeng, D., Li, W., Guo, S., Zomaya, A.Y., & Jin, H. (2020). Intelligent VNF orchestration and flow scheduling via model-assisted deep reinforcement learning. Intelligent VNF orchestration and flow scheduling via model-assisted deep reinforcement learning, 38(2), 279\u2013291.","journal-title":"Intelligent VNF orchestration and flow scheduling via model-assisted deep reinforcement learning"},{"key":"2883_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Gao, L., Ren, J., et al. (2019). Optimization for mobile streaming media based on deep Q-learning. In: 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), 2019: 285-290.","DOI":"10.1109\/CBD.2019.00058"},{"issue":"6","key":"2883_CR28","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.trb.2004.07.004","volume":"39","author":"NJ van der Zijpp","year":"2005","unstructured":"van der Zijpp, N. J., & Catalano, S. F. (2005). Path enumeration by finding the constrained K-shortest paths. Transportation Research Part B-Methodological, 39(6), 545\u2013563.","journal-title":"Transportation Research Part B-Methodological"},{"key":"2883_CR29","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.trb.2015.04.002","volume":"81","author":"A Khani","year":"2015","unstructured":"Khani, A., & Boyles, S. D. (2015). An exact algorithm for the mean-standard deviation shortest path problem. Transportation Research Part B-Methodological, 81, 252\u2013266.","journal-title":"Transportation Research Part B-Methodological"},{"issue":"1","key":"2883_CR30","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s11277-020-07060-4","volume":"112","author":"MT Islam","year":"2020","unstructured":"Islam, M. T., Islam, N., & Al Refat, M. D. (2020). Node to node performance evaluation through RYU SDN controller. Wireless Personal Communications, 112(1), 555\u2013570.","journal-title":"Wireless Personal Communications"},{"key":"2883_CR31","doi-asserted-by":"crossref","unstructured":"Froemmgen, A., Stohr, D., Fornoff, J., Effelsberg, W., & Buchmann, A.P. (2016). Capture and replay: Reproducible network experiments in Mininet, In: Proceedings of the 2016 ACM Conference on Special Interest Group on Data Communication (SIGCOMM '16), 2016: pp 621\u2013622.","DOI":"10.1145\/2934872.2959076"},{"key":"2883_CR32","doi-asserted-by":"crossref","unstructured":"Lam, J., Lee, S.G., Andrianto, V.C. (2017). Secure switch migration protocol with OpenFlow. In: Proceedings of the 2017 International Conference on Information Technology (ICIT 2017), 2017: pp 171\u2013174.","DOI":"10.1145\/3176653.3176682"},{"issue":"11","key":"2883_CR33","doi-asserted-by":"publisher","first-page":"6723","DOI":"10.1109\/TSMC.2020.2963943","volume":"51","author":"L Ma","year":"2021","unstructured":"Ma, L., Cheng, S., & Shi, Y. (2021). Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Transactions on Systems Man Cybernetics-Systems, 51(11), 6723\u20136742.","journal-title":"IEEE Transactions on Systems Man Cybernetics-Systems"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02883-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-021-02883-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-021-02883-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T15:51:20Z","timestamp":1720021880000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-021-02883-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,17]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2883"],"URL":"https:\/\/doi.org\/10.1007\/s11276-021-02883-w","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"value":"1022-0038","type":"print"},{"value":"1572-8196","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,17]]},"assertion":[{"value":"20 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}