Samuel Champagne
Malcolm Heywood
ABSTRACT
The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.
- 2017. Abilene Backbone Network. Internet2. (2017). https://www.internet2.eduGoogle Scholar
- Md. Faizul Bari, Arup Raton Roy, Shihabur Rahman Chowdhury, Qi Zhang, Mohamed Faten Zhani, Reaz Ahmed, and Raouf Boutaba. 2013. Dynamic Controller Provisioning in Software Defined Networks. In International Conference on Network and Service Management. 18--25.Google Scholar
- H. Bo, W. Chuan'an, and W. Ying. 2016. The controller placement problem for software-defined networks. In IEEE International Conference on Computer and Communications. 2435--2439.Google Scholar
- Brandon Heller, Rob Sherwood, and Nick McKeown. 2012. The controller placement problem. Computer Communication Review 42, 4 (2012), 473--478. Google ScholarDigital Library
- David Hock, Steffen Gebert, Matthias Hartmann, Thomas Zinner, and Phuoc Tran-Gia. 2014. POCO-framework for Pareto-optimal resilient controller placement in SDN-based core networks. In IEEE Network Operations and Management Symposium. 1--2.Google ScholarCross Ref
- Ahmad Jalili, Vahid Ahmadi, Manijeh Keshtgari, and Morteza Kazemi. 2015. Controller Placement in Software-Defined WAN Using Multi Objective Genetic Algorithm. In International Conference on Knowledge-based Engineering and Innovation. 656--662.Google Scholar
- Rajeev Kumar and Peter Rockett. 2002. Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation 10, 3 (2002), 283--314. Google ScholarDigital Library
- S. Liang, A.N. Zincir-Heywood, and M.I. Heywood. 2006. Adding more intelligence to the network routing problem: AntNet and Ga-agents. Applied Soft Computing 6, 3 (2006), 244--257. Google ScholarDigital Library
- Adetokunbo Makanju, A. Nur Zincir-Heywood, and Shinsaku Kiyomoto. 2017. On evolutionary computation for moving target defense in software defined networks. In ACM Genetic and Evolutionary Computation Conference. 287--288. Google ScholarDigital Library
- A. Sabeegh, Y. Al-Dunainawi, M. F. Abbod, and H. S. Al-Raweshidy. 2016. A hybrid intelligent approach for optimising software-refined networks performance. In International Conference on Information Communication and Management. 47--51.Google Scholar
- Jean-Michel Sanner, Yassine Hadjadj Aoul, Meryem Ouzzif, and Gerardo Rubino. 2017. An evolutionary controllers' placement algorithm for reliable SDN networks. In International Conference on Network and Service Management. 1--6.Google ScholarCross Ref
- G. Wang, Z. Zhao, J. Peng, R. Li, and H. Zhang. 2014. An approximate algorithm of controller configuration in multi-domain SDN architecture. In International Conference on Communications and Networking in China. 601--605.Google Scholar
Index Terms
- A genetic algorithm for dynamic controller placement in software defined networking
Recommendations
Survival backup strategy for controller placement problem in Software Defined Networking
AbstractThe core concept of Software Defined Networking (SDN) is to abstract the control layer from the data layer. SDN architectures can provide programmatic interfaces in communication networks that significantly simplify network management and improve ...
Performance Evaluation Using RYU SDN Controller in Software-Defined Networking Environment
AbstractSoftware-defined networking (SDN) is a new approach that overcomes the obstacles which are faced by conventional networking architecture. The core idea of SDN is to separate the control plane from the data plane. This idea improves the network in ...
Software defined networking: State-of-the-art
In the fast moving space of technology, everything and everyone is on the Internet accessing the world. The creation of large networks and data centers required for managing and analyzing data will increase in the future, so traditional networking ...
Comments