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Application of Artificial Intelligence to Software Defined Networking: A Survey

Affiliations

  • Department of Computer Engineering, Ege University, Bornova, 35100, İzmir, Turkey

Abstract


Background/Objectives: This paper surveys the application of Artificial Intelligence (AI) to the Software Defined Networking (SDN) paradigm which is a part of previous efforts to give the computer networks the ability of being programmed based on the separation between the control and forwarding planes. In SDN approach, the controller represents the central brain of the network which leads to an advanced level of flexibility and network intelligence. Methods/Statistical Analysis: Different artificial intelligence-based techniques have been applied to achieve an enhanced load balance, network security and intelligent network applications in the SDN approach. Findings: Ant colony algorithms were successful in increasing the maximal Quality of Experience (QoE) by 24.1% compared with the shortest path routing approach. Neural network based intrusion prevention system has shown a scalable performance with low false positive rate. Applying reinforcement learning based technique in adaptive video streaming system compared with the shortest path routing and greedy-based approaches has shown decreasing of the frame loss rate by 89% and 70% respectively. Applications/Improvements: This study highlights the first attempts for applying artificial intelligence in SDN paradigm. However, hybrid intelligent techniques could be the key for achieving more advanced behaviour in SDN-based networks.

Keywords

Artificial Intelligence (AI), OpenFlow, Software Defined Networking (SDN).

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References


  • Open Networking Foundation. Software-defined networking: The new norm for networks. Available from: https://www.opennetworking.org/images/stories/downloads/sdn-resources/white-papers/wp-sdn-newnorm.pdf
  • Astuto BN, Mendonca M, Nguyen XN, Obraczka K, Turletti T. A survey of software-defined networking: past, present, and future of programmable networks. IEEE Communications Surveys and Tutorials. 2014; 16(3):1617–34.
  • Bakshi K. Considerations for Software Defined Networking (SDN): Approaches and use cases. IEEE Aerospace Conference, Big Sky; MT. 2013 Mar. p. 1–9.
  • Shinde MB, Tamhankar SG. Review: software defined networking and OpenFlow. International Journal of Scientific Research in Network Security and Communication. 2013Jun; 1(2):18–20.
  • Feamster N, Zegura E, Rexford J. The road to SDN: An intellectual history of programmable networks. ACM SIGCOMM Computer Communication Review archive. 2014; 44(2):87–98.
  • Kreutz D, Ramos FMV, Verissimo P, Rothenberg CE, Azodolmolky S, Uhlig S. Software-defined networking: A comprehensive survey. Proceedings of the IEEE. 2015; 103(1):14–76.
  • Basta A, Kellerer W, Hoffmann M, Hoffmann K, Schmidt E-D. A virtual SDN-enabled LTE EPC architecture: A case study for S-/P-gateways functions. Future Networks and Services (SDN4FNS); Trento. 2013 Nov. p. 1–7.
  • Jammal M, Singh T, Shami A, Asal R, Li Y. Software defined networking: State of the art and research challenges. Computer Networks. 2014; 72:74–98.
  • Rowshanrad S, Namvarasl S, Abdi V, Hajizadeh M, Keshtgary M. A survey on SDN, the future of networking. Journal of Advanced Computer Science and Technology. 2014; 3(2): 232–48.
  • Braun W, Menth M. Software-defined networking using OpenFlow: Protocols, applications and architectural design choices. Future Internet. 2014; 6(2):302–36.
  • OpenFlow Switch Specification Version 1.0.0. Available from: https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.0.0.pdf
  • OpenFlow Switch Specification Version 1.1.0. Available from: http://archive.openflow.org/documents/openflow-spec-v1.1.0.pdf
  • OpenFlow Switch Specification Version 1.2.0. Available from: https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.2.pdf
  • OpenFlow Switch Specification Version 1.3.0. Available from: https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.3.0.pdf
  • OpenFlow Switch Specification Version 1.4.0. Available from:
  • https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.4.0.pdf
  • FlowVisor. Available from: https://github.com/OPENNETWORKINGLAB/flowvisor/wiki
  • NOX. Available from: http://archive.openflow.org/downloads/Workshop2009/OpenFlowWorkshop-MartinCasado.pdf
  • POX. Available from: https://en.wikipedia.org/wiki/Pox
  • Beacon. Available from: https://openflow.stanford.edu/display/Beacon/Home
  • Ng TSE, Cai Z, Cox AL. Maestro: A system for scalable OpenFlow control. Available from: http://www.cs.rice.edu/~eugeneng/papers/TR10-11.pdf 21. Floodlight OpenFlow Controller - Project Floodlight. Available from: http://www.projectfloodlight.org/floodlight/
  • Announcing release of Floodlight with OF 1.3 support. Available from: http://sdnhub.org/releases/floodlight-plus-openflow13-support/
  • Ryu 3.9 documentation. Available from: http://ryu.readthedocs.org/en/latest/getting_started.html#what-s-ryu.
  • Open day light. Available from: http://www.opendaylight.org/
  • Gorsansson P, Black C. Software defined networks - A comprehensive approach.1st ed. Morgan Kaufmann, an imprint of Elsevier; 2014.
  • Eadala SY, Nagarajan V. A review on deployment architectures of path computation element using software defined networking paradigm. Indian Journal of Science and Technology. 2016 Feb; 9(10). DOI: 10.17485/ijst/2016/v9i10/84944.
  • Mandekar AV, Chandramouli K. Centralization of network using openflow protocol. Indian Journal of Science and Technology. 2015 Jan; 8(S2). DOI: 10.17485/ijst/2015/v8iS2/61217.
  • Mittal P, Singh Y. Development of intelligent transportation system for improving average moving and waiting time with artificial intelligence. Indian Journal of Science and Technology. 2016 Jan; 9(3). DOI: 10.17485/ijst/2016/v9i3/84156.
  • Davis B. Leveraging the load balancer to fight DDoS. Available from:
  • http://www.sans.org/reading-room/whitepapers/firewalls/leveraging-load-balancer-fight-ddos-33408
  • Califano A, Dincelli E, Goel S. Using features of cloud computing to defend smart grid against DDoS attacks. 10th Annual symposium on information assurance (Asia 15), ALBANY; 2015Jun. p. 44–50.
  • Chen-Xiao C, Ya-Bin X. Research on load balance method in SDN. International Journal of Grid and Distributed Computing. 2016; 9(1):25–36.
  • Ruelas AMR, Rothenberg CE. Implementation of neural switch using OpenFlow as load balancing method in data center. Campinas, Brasil: University of Campinas; 2015.
  • Chou L-D, Yang Y-T, Hong Y-M, Hu J-K, Jean B. A genetic-based load balancing algorithm in openflow network. Advanced Technologies, Embedded and Multimedia for Human-centric Computing. 2013; 260:411–7.
  • Balaguer R. Flow embedding algorithms for software defined audio networks [Master thesis]. Zurich, Switzerland: Swiss Federal Institute of Technology. Available from: http://ftp.tik.ee.ethz.ch/pub/.../MA-2014-14.pdf
  • Dobrijevic O, Santl M, Matijasevic M. Ant colony optimization for QoE-centric flow routing in software-defined networks. 2015 11th International Conference on Network and Service Management (CNSM); Barcelona. 2015 Nov. p. 274–8.
  • Latah M. Solving multiple TSP problem by K-means and crossover based modified ACO algorithm. IJERT. 2016 Feb; 5(2):430–4.
  • Akhunzada A, Ahmed E, Gani A, Khan MK, Imran M, Guizani S. Securing the software defined networks: taxonomy, requirements, and open issues. IEEE Communications Magazine. 2015Apr; 53(4):36 –44.
  • Jankowski D, Amanowicz M. Intrusion detection in software defined networks with self-organized maps. Journal of Telecommunications and Information Technology. 2015; 4:3–9.
  • Dabbagh M, Hamdaoui B, Guizaniy M, Rayes A. Software-defined networking security: Pros and cons. IEEE Communications Magazine. 2015; 53(6):73–9.
  • Bai H. A survey on artificial intelligence for network routing problems. NM,USA: University of New Mexico; 2007.
  • Mustafa U, Masud MM, Trabelsi Z, Wood T, Al Harthi Z. Firewall performance optimization using data mining techniques. 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC); Sardinia. 2013 Jul. p. 934–40.
  • Mukherjee D, Acharyya S. Ant colony optimization technique applied in network routing problem. International Journal of Computer Applications. 2010; 1(15):66–73.
  • Dotcenko S, Vladyko A, Letenko I. A fuzzy logic-based information security management for software-defined networks. 16th International Conference on Advanced Communication Technology (ICACT); Pyeongchang. 2014 Feb. p. 167–71.
  • Mikians J, Barlet-Ros P, Sanjuas-Cuxart J, Sol´e-Pareta J. A practical approach to portscan detection in very high-speed links. PAM'11 Proceedings of the 12th International Conference on Passive and Active Measurement; Atlanta. 2011Mar. p. 112–21.
  • Williamson MM. Throttling viruses: Restricting propagation to defeat malicious mobile code. Proceedings 18th Annual Computer Security Applications Conference; Las Vegas. 2002 Dec. p. 61–8.
  • Mehdi SA, Khalid J, Khayam SA. Revisiting traffic anomaly detection using software defined networking. RAID'11 Proceedings of the 14th International Conference on Recent Advances in Intrusion Detection; California. 2011Sep. p. 161–80.
  • Chen X-F, Yu S-Z. CIPA: A collaborative intrusion prevention architecture for programmable network and SDN. Computers and Security. 2016; 58:1–19.
  • Braga R, Mota E, Passito A. Lightweight DDoS flooding attack detection using NOX/OpenFlow. 2010 IEEE 35th Conference on Local Computer Networks (LCN); Denver, CO. 2010 Oct. p. 408–15.
  • Deepa SN, Devi BA. A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology. 2011 Nov; 4(11). DOI: 10.17485/ijst/2011/v4i11/30291.
  • Yan Q, Yu FR, Gong Q, Li J. Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) attacks in cloud computing environments: a survey, some research issues, and challenges. IEEE Communications Surveys and Tutorials. 2016; 18(1):602–22.
  • Uzakgider T, Cetinkaya C, Sayit M. Learning-based approach for layered adaptive video streaming over SDN. Computer Networks. 2015; 92(P2):357–68.

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