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Magneto approach to QoS monitoring | IEEE Conference Publication | IEEE Xplore

Magneto approach to QoS monitoring


Abstract:

Quality of Service (QoS) monitoring of end-user services is an integral and indispensable part of service management. However in large, heterogeneous and complex networks...Show More

Abstract:

Quality of Service (QoS) monitoring of end-user services is an integral and indispensable part of service management. However in large, heterogeneous and complex networks where there are many services, many types of end-user devices, and huge numbers of subscribers, it is not trivial to monitor QoS and estimate the status of Service Level Agreements (SLAs). Furthermore, the overwhelming majority of end-terminals do not provide precise information about QoS which aggravates the difficulty of keeping track of SLAs. In this paper, we describe a solution that combines a number of techniques in a novel and unique way to overcome the complexity and difficulty of QoS monitoring. Our solution uses a model driven approach to service modeling, data mining techniques on small sample sets of terminal QoS reports (from “smarter” end-user devices), and network level key performance indicators (N-KPIs) from probes to address this problem. Service modeling techniques empowered with a modeling engine and a purpose-built language hide the complexity of SLA status monitoring. The data mining technique uses its own engine and learnt data models to estimate QoS values based on N-KPIs, and feeds the estimated values to the modeling engine to calculate SLAs. We describe our solution, the prototype and experimental results in the paper.
Date of Conference: 23-27 May 2011
Date Added to IEEE Xplore: 18 August 2011
ISBN Information:
Print ISSN: 1573-0077
Conference Location: Dublin, Ireland

References

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