Traffic engineering in software defined networks

By Sugam Agarwal, M. Kodialam, T. V. Lakshman. 2013 Proceedings IEEE INFOCOM, 2013

551 citations recorded by the source corpus.

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A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “The incremental deployment [228], [229] is a feasible solution.”
A roadmap for traffic engineering in SDN-OpenFlow networks (2014, background): “…Defined Networking (SDN) is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution.”
Hybrid SDN Networks: A Survey of Existing Approaches (2018, unknown): “[102].”
Survey on Network Virtualization Hypervisors for Software Defined Networking (2015, background): “The current statistics can serve as basis for network traffic estimation and monitoring [215]–[217] as well as dynamic adoptions by SDN applications and traffic engineering [218],”
A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation (2014, background): “add_switch (name = “A”, ports [1,2,3,4])”
Experience-driven Networking: A Deep Reinforcement Learning based Approach (2018, background): “2) Related works [2], [13], [17] have not explicitly addressed end-to-end delay, which, however, is one of the major concerns of this paper.”