The Probabilistic Network Management (PNM) paradigm specifies non-deterministic methods in which decisions are based on probabilistic objectives and richer statistical monitoring information, rather than on strict performance guarantees and measurements.
Compared to current network management technology, PNM approaches provide new effective means to resource-efficient and flexible network management solutions. This is achieved by combining statistical modeling with adaptive mechanisms and decentralized operation. Decentralized probabilistic management enables prediction of network performance, adaptivity to network variations, robustness and improved failure-resilience, thereby allowing for optimized and cost-efficient network operation and management.
We currently develop PNM approaches within the following areas:
For more information, please contact:
Daniel Gillblad, dgi [at] sics.se
Rebecca Steinert, rebste [at] sics.se
A new group has been formed at the DNA lab that will focus on applied machine learning and statistical analysis for the telecom domain. The Network Intelligence group will be headed by Rebecca Steinert and the research will be developed together with lab members and associates currently working with her in existing projects. The group will establish new lines of research involving monitoring, probabilistic management, virtualization and software-defined networking for various networking...
Thesis title: Probabilistic Fault Management in Networked Systems Date: Wednesday, May 28, 2014 Time: 14:00 Location: Sal F3, KTH, Lindstedtsvägen 26, Stockholm Opponent: Professor Olivier Festor, School of Engineering in Information Technology Telecom Nancy Committee: Dr. Malgorzata Steinder, IBM T. J. Watson Research Center, NY Professor Olov Schelén, LTU - Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering Professor Gunnar Karlsson, KTH –...