Self-aware Services of NGSDP:Using Bayesian Networks for

Measuring Quality of Convergent Services

Zhi Luo, Junping Wang and Qiliang Zhu

School of Software Engineering

Beijing University of Posts and Telecommunications

Beijing, China

luozhi1989@gmail.com, wangjunping@bupt.edu.cn, zhuqiliang@buptnet.edu.com

Abstract

We propose a general architecture and implementation for the autonomous assessment of quality

of arbitrary service elements in the convergent service environments. We describe a quality engine,

which is the central component of our proposed architecture of self-aware convergent services of

NGSDP. The quality engine combines domain independent statistical analysis and probabilistic reasoning

technology (Bayesian networks) with domain dependent measurement collection and evaluation

methods. The resultant probabilistic assessment can be transported via network protocols in

the convergent services and it enables non-hierarchical communications about the quality of service

elements. We demonstrate the validity of our approach using Multimedia Messaging Service (MMS)

Relay/Server and detecting anomalies: storage overflow and message expiration.