Scalable in-network rate monitoring
| Autor: | Rebecca Steinert, Per Kreuger |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
congestion detection
Computer and Information Sciences link utilization modelling Computer science Estimation theory Real-time computing Detector Information and Computer Science Data- och informationsvetenskap Method of moments (statistics) Increased risk performance monitoring statistical traffic analysis Log-normal distribution Scalability in-network rate monitoring Simulation probabilistic management |
| Zdroj: | IM |
| Popis: | 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. Unify |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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