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Sensors 2018, 18(4), 1079;

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks

Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases 9, Ciudad Universitaria, 28040 Madrid, Spain
Department of Convergence Security, Sungshin Women’s University, 249-1 Dongseon-Dong 3-ga, Seoul 136-742, Korea
Author to whom correspondence should be addressed.
Those authors contributed equally to this work.
Received: 30 January 2018 / Revised: 27 March 2018 / Accepted: 31 March 2018 / Published: 3 April 2018
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
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Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches. View Full-Text
Keywords: clustering; data plane; flow conservation; software defined networks; statistics; videostreaming clustering; data plane; flow conservation; software defined networks; statistics; videostreaming

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Puente Fernández, J.A.; García Villalba, L.J.; Kim, T.-H. Clustering and Flow Conservation Monitoring Tool for Software Defined Networks. Sensors 2018, 18, 1079.

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