Optimizing Traffic Engineering in Software Defined Networking

The digital society is an outcome of the Internet which has nearly made everything connected and accessible no matter where or when. Nevertheless, despite the fact that conventional IP networks are complicated and very hard to manage, they are still widely adopted. The already established policies make the network configuration/reconfiguration a complex process that reacts to errors, load, and modifications. The prevailing networks are vertically integrated which makes things more and more complicated: Data planes and control are strapped together. Software-defined networking is a model that is meant to solve this issue by splitting the vertical integration and detaching the networks control logic from the implicit routers and switches; this could be achieved by reinforcing centralization of network control and making the network programmable. In this work, we worked to implement MPLS networks with SDN, to enhance the traffic engineering over the network, and to minimize the network delay and latency, with minimum cost using three of the different SDN networks. The experiment results showed the advantage of the proposed approach for reducing the network delay, comparing with previous studies. Where the average of network delay in our approach reaches to 3.01 milliseconds.

[1]  Siddharth Bhatia,et al.  MPLS based hybridization in SDN , 2017, 2017 Fourth International Conference on Software Defined Systems (SDS).

[2]  Hamid Farhadi,et al.  Rethinking Flow Classification in SDN , 2014, 2014 IEEE International Conference on Cloud Engineering.

[3]  Keith Kirkpatrick,et al.  Software-defined networking , 2013, CACM.

[4]  Mohammed Ghanbari,et al.  An efficient algorithm to create a loop free backup routing table , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[5]  A. Neeraja,et al.  Licensed under Creative Commons Attribution Cc by Improving Network Management with Software Defined Networking , 2022 .

[6]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[7]  Katsunori Yamaoka,et al.  A flow aggregation method based on end-to-end delay in SDN , 2017, 2017 IEEE International Conference on Communications (ICC).

[8]  Huijuan Wang,et al.  Application of Dijkstra algorithm in robot path-planning , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

[9]  Ian F. Akyildiz,et al.  A roadmap for traffic engineering in SDN-OpenFlow networks , 2014, Comput. Networks.

[10]  Nader Mokari,et al.  Optimal Qos-aware network reconfiguration in software defined cloud data centers , 2017, Comput. Networks.

[11]  Hyunseung Choo,et al.  Enhanced local detouring mechanisms for rapid and lightweight failure recovery in OpenFlow networks , 2017, Comput. Commun..

[12]  Seifedine Kadry,et al.  On The Optimization of Dijkstras Algorithm , 2012, ArXiv.

[13]  Kamal Benzekki,et al.  Software-defined networking (SDN): a survey , 2016, Secur. Commun. Networks.

[14]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[15]  Hamid Farhadi,et al.  Software-Defined Networking: A survey , 2015, Comput. Networks.

[16]  Kostas Katrinis,et al.  MiceTrap: Scalable traffic engineering of datacenter mice flows using OpenFlow , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[17]  Chu-Sing Yang,et al.  A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing , 2015, Neural Computing and Applications.

[18]  Reza Nejabati,et al.  Transport Network Orchestration for End-to-End Multilayer Provisioning Across Heterogeneous SDN/OpenFlow and GMPLS/PCE Control Domains , 2015, Journal of Lightwave Technology.

[19]  Slawomir Kuklinski,et al.  MSDN-TE: Multipath Based Traffic Engineering for SDN , 2016, ACIIDS.

[20]  Eiji Oki,et al.  Flows Reduction Scheme Using Two MPLS Tags in Software-Defined Network , 2017, IEEE Access.

[21]  Jim Esch,et al.  Software-Defined Networking: A Comprehensive Survey , 2015, Proc. IEEE.

[22]  Yonggang Wen,et al.  “ A Survey of Software Defined Networking , 2020 .

[23]  Ajay Guleria,et al.  Traffic Engineering in Software Defined Networks: A Survey , 2016 .

[24]  Hanife Apaydin Ozkan SHORTEST PATH ALGORITHMS FOR PETRI NETS , 2016 .

[26]  Luis Bernardo,et al.  SDN based traffic engineering without optimization: A centrality based approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[27]  Tao Jin,et al.  Application-awareness in SDN , 2013, SIGCOMM.

[28]  Nalini Venkatasubramanian,et al.  A Software Defined Networking architecture for the Internet-of-Things , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[29]  Murali S. Kodialam,et al.  Traffic engineering in software defined networks , 2013, 2013 Proceedings IEEE INFOCOM.

[30]  S. Sakr,et al.  CLASSIFICATION OF VOIP AND NON-VOIP TRAFFIC USING MACHINE LEARNING APPROACHES , 2016 .