A Hierarchical Distributed Control Plane for Path Computation Scalability in Large Scale Software-Defined Networks

The architecture of the high availability distributed control plane for SDN/OpenFlow networks are considered. High availability is achieved by redundancy of controller instances, active switch-controller communications, computing resources and tools for a controller instance failure and overloading detection and recovery. The proactive backup controller allocation algorithm which allows to minimize the time to repair in the case of a single controller instance failure is discussed. The algorithm for controller load-balancing allows dynamically reconfigure the control plane with a minimum number of switch control transfer operations to avoid controller instance overloading. The initial experimental results of the proposed algorithms for the HA distributed SDN control plane are described.

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Scalable distributed control plane for On-line social networks support cognitive neural computing in software defined networks

Future Generation Computer Systems ◽

10.1016/j.future.2018.01.054 ◽

2019 ◽

Vol 93 ◽

pp. 993-1001 ◽

Cited By ~ 1

Author(s):

Lingxia Liao ◽

Chin-Feng Lai ◽

Jaifu Wan ◽

Victor C.M. Leung ◽

Tien-Chi Huang

Keyword(s):

Social Networks ◽

Distributed Control ◽

Software Defined Networks ◽

Control Plane ◽

Neural Computing ◽

On Line

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Augmented Affinity Propagation-Based Network Partitioning for Multiple Controllers Placement in Software Defined Networks

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8655 ◽

2020 ◽

Vol 17 (1) ◽

pp. 228-233

Author(s):

C. N. Sminesh ◽

E. Grace Mary Kanaga ◽

A. G. Sreejish

Keyword(s):

Large Scale ◽

Affinity Propagation ◽

Software Defined Networks ◽

Network Partitioning ◽

Control Plane ◽

Placement Problem ◽

Large Scale Network ◽

Scale Network ◽

Controller Placement Problem ◽

Multiple Controllers

Software Defined Networks (SDN) divide network intelligence and packet forwarding functionalities between control plane and data plane devices respectively. Multiple controllers need to be deployed in the control plane in large SDN networks to improve performance and scalability. In a multi-controller scenario, finding the adequate number of controllers and their load distribution are open research challenges. In a large-scale network, the control plane load balancing is termed a controller placement problem (CPP). It is observed that of the existing solutions for the CPP, clustering-based approaches provide computationally less intensive solutions. The proposed augmented affinity propagation (augmented-AP) clustering identifies the required number of network partitions and places the controllers such that the distribution of switches to the controller is much better than with existing algorithms. The simulation results show that the computed controller imbalance factor of augmented-AP algorithm outperforms the existing k-means algorithm.

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