A tutorial on learning with Bayesian networks
Learning in graphical models
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Measurement-based admission control at edge routers
IEEE/ACM Transactions on Networking (TON)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
ADMISSION CONTROL IN MULTI-SERVICE IP NETWORKS: A TUTORIAL
IEEE Communications Surveys & Tutorials
Adaptive provisioning of differentiated services networks based on reinforcement learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Key research challenges in network management
IEEE Communications Magazine
An autonomic open marketplace for service management and resilience
Proceedings of the 7th International Conference on Network and Services Management
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The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application.