QoC-Aware Control Traffic Engineering in Software Defined Networks

Sridharan, Vignesh; Mohan, Ponnusamy; Gurusamy, Mohan

doi:10.1109/tnsm.2019.2940863

Cited by 7 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [22], the authors classified the controllers' traffic into Low and High based on the number of packets and number of bytes received by the controller using Artificial Neural Network (ANN) and predicted the controller traffic. In [23], the authors proposed a methodology to improve the quality of controller by connecting switches to more than one controller in 70-30 ratio. They improved the performance of controller in reliability context.…”

Section: Sdn Traffic Managementmentioning

confidence: 99%

Prediction Based Dynamic Controller Placement in SDN

Ramya¹,

Manoharan²

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

The current technologies such as IoT, 5G networks and Fog computing creates a challenge in the efficient management of devices in dynamic conditions. Software-Defined Network (SDN) has been defined as a promising solution for providing efficient network management by decoupling the data and control planes from the network devices and enables programmability of network devices. The major challenge in SDN is identification of number of controllers to be placed and its optimal placements in the network. To address this issue, this work proposes a Traffic Engineering mechanism that leverages the performance of Machine Learning to predict controller numbers by analysing and predicting the controller's traffic. The optimal locations of controllers are identified by using the K-Means++ algorithm. The proposed method is simulated using Mininet and the results depict that the proposed methodology outperforms the existing methodologies in terms of performance parameters.

show abstract

Section: Sdn Traffic Managementmentioning

confidence: 99%