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Scalable in-network rate monitoring
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
Number of Authors: 2
2015 (English)Conference paper (Refereed)
Abstract [en]

We propose a highly scalable statistical method for modelling the monitored traffic rate in a network node and suggest a simple method for detecting increased risk of congestion at different monitoring time scales. The approach is based on parameter estimation of a lognormal distribution using the method of moments. The proposed method is computation- ally efficient and requires only two counters for updating the parameter estimates between consecutive inspections. Evaluation using a naive congestion detector with a success rate of over 98% indicates that our model can be used to detect episodes of high congestion risk at 0.3 s using estimates captured at 5 m intervals.

Place, publisher, year, edition, pages
2015, 6.
Keyword [en]
probabilistic management, performance monitor- ing, statistical traffic analysis, link utilization modelling, congestion detection, in-network rate monitoring
National Category
Computer and Information Science
URN: urn:nbn:se:ri:diva-15645OAI: diva2:1036962
IFIP/IEEE Integrated Network Management --- IM'15
Available from: 2016-10-13 Created: 2016-10-13

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Kreuger, Per
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