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Representative Measurement Point Selection to Monitor Software-defined Networks

Hark, Rhaban and Ghanmi, Mohamed and Kar, Sounak and Richerzhagen, Nils and Rizk, Amr and Steinmetz, Ralf (2018):
Representative Measurement Point Selection to Monitor Software-defined Networks.
Chicago, USA, IEEE, In: The 43nd IEEE Conference on Local Computer Networks (LCN), Chicago, USA, [Online-Edition: https://www.ieeelcn.org/Program_technical.html],
[Conference or Workshop Item]

Abstract

Network state monitoring is a fundamental task for network management. However, determining the full network state in Software defined Networks requires disproportionately too many resources. This stems from the discrepancy between the established methods used for state monitoring compared to the varying contribution in terms of information obtained from every additionally monitored network node. This relationship may even become more complicated depending on the network state information of interest. One solution to overcome bottlenecks by reducing the overall monitoring footprint is the use of spatial sampling, which allows the estimation of the network state based a fraction of the overall state. In this work, we propose schemes to place a small number of measurement points in the SDN network to maximize the obtained network state information. Considering different conditions, we utilize routing information and graph theoretic centrality metrics, respectively, to estimate the amount of information a node provides. Based on this knowledge, we, furthermore develop a mechanism to place multiple measurement points while avoiding redundant measurements. For demonstration purpose, we use the developed mechanisms to estimate the Flow Size Distribution in SDN environments. An emulative evaluation taking several known topologies shows the effectiveness of spatial sampling using the proposed scheme.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Hark, Rhaban and Ghanmi, Mohamed and Kar, Sounak and Richerzhagen, Nils and Rizk, Amr and Steinmetz, Ralf
Title: Representative Measurement Point Selection to Monitor Software-defined Networks
Language: English
Abstract:

Network state monitoring is a fundamental task for network management. However, determining the full network state in Software defined Networks requires disproportionately too many resources. This stems from the discrepancy between the established methods used for state monitoring compared to the varying contribution in terms of information obtained from every additionally monitored network node. This relationship may even become more complicated depending on the network state information of interest. One solution to overcome bottlenecks by reducing the overall monitoring footprint is the use of spatial sampling, which allows the estimation of the network state based a fraction of the overall state. In this work, we propose schemes to place a small number of measurement points in the SDN network to maximize the obtained network state information. Considering different conditions, we utilize routing information and graph theoretic centrality metrics, respectively, to estimate the amount of information a node provides. Based on this knowledge, we, furthermore develop a mechanism to place multiple measurement points while avoiding redundant measurements. For demonstration purpose, we use the developed mechanisms to estimate the Flow Size Distribution in SDN environments. An emulative evaluation taking several known topologies shows the effectiveness of spatial sampling using the proposed scheme.

Place of Publication: Chicago, USA
Publisher: IEEE
Uncontrolled Keywords: monitor placement, software-defined networking, flow size distribution
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Multimedia Communications
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms > Subpproject B1: Monitoring and Analysis
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > B: Adaptation Mechanisms > Subproject B4: Planning
Event Title: The 43nd IEEE Conference on Local Computer Networks (LCN)
Event Location: Chicago, USA
Date Deposited: 03 Aug 2018 09:28
Official URL: https://www.ieeelcn.org/Program_technical.html
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