Science AreasProbabilistic Management

Probabilistic Management

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:

  • Mobility - prediction of mobility patterns and paging sequences
  • Network Monitoring - autonomous detection and localization of network faults and anomalies, change detection and event correlation
  • Goal Translation - mapping of high-level goals to low-level parameters with probabilistic feedback to maintain SLAs
  • Distributed Data Management - efficient caching and methods targeting management of big data
  • Data Analysis - mining of event processes; detection of faults, anomalies and trend changes; event correlation
  • Approaches developed under the PNM paradigm target the challenges in future networked systems (e.g. scalability, adaptivity, controllability, etc), and encompass features that are highly valuable for dynamic networks such as mobile systems, sensor networks, Internet of Things, and cross-linked systems such as network clouds.

For more information, please contact:

Daniel Gillblad, dgi [at]

Rebecca Steinert, rebste [at]


Björn Bjurling
PhD, Senior Researcher

bgb [at]