{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:11:55Z","timestamp":1767262315708,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T00:00:00Z","timestamp":1531094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502008"],"award-info":[{"award-number":["61502008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.<\/jats:p>","DOI":"10.3390\/fi10070064","type":"journal-article","created":{"date-parts":[[2018,7,9]],"date-time":"2018-07-09T11:18:53Z","timestamp":1531135133000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN"],"prefix":"10.3390","volume":"10","author":[{"given":"Dong","family":"Sun","sequence":"first","affiliation":[{"name":"Experimental Management Center, Henan Institute of Technology, Xinxiang 453000, China"}]},{"given":"Kaixin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Henan Institute of Technology, Xinxiang 453003, China"}]},{"given":"Yaming","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei 230039, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7258-3418","authenticated-orcid":false,"given":"Jie","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei 230039, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MNET.2016.7389832","article-title":"When big data meets software-defined networking: SDN for big data and big data for SDN","volume":"30","author":"Cui","year":"2016","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lan, Y.L., Wang, K., and Hsu, Y.H. 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