Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/INM.2011.5990693 | Integrated Network Management |
Keywords | Field | DocType |
data mining,data models,monitoring,quality of service,telecommunication computing,telecommunication network management,N-KPI,QoS monitoring,SLA status monitoring,data mining techniques,learnt data models,magneto approach,network level key performance indicators,quality of service,service level agreements,service management,service modeling techniques,IPTV,QoS,SLA,data-mining,network-KPI,service-modeling,terminal-reports | Data modeling,Mobile QoS,Service management,Performance indicator,Service level,Computer science,Quality of service,Computer network,Complex network,IPTV,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4244-9220-6 | 11 | 0.95 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sidath Handurukande | 1 | 14 | 3.05 |
| Szymon Fedor | 2 | 90 | 7.25 |
| Stefan Wallin | 3 | 59 | 8.01 |
| Martin Zach | 4 | 29 | 3.07 |