Causality for Networking Applications

Communication networks are complex systems whose operation relies on a large number of components that work together to provide services to end users. As the quality of these services depends on different parameters, understanding how each of them impacts the final performance of a service is a challenging but important problem. However, intervening on individual factors to evaluate the impact of the different parameters is often impractical due to the high cost of intervention in a network. It is, therefore, desirable to adopt a formal approach to understand the role of the different parameters and to predict how a change in any of these parameters will impact performance. The approach of causality provides a powerful framework to investigate these questions. At EURECOM, we work on advancing the state-of-the-art on this topic by working with real-world data and extending the application of causality to complex systems in the area of telecommunication networks, for which common assumptions of normality, linearity and discrete data do no hold. Topics include:

  • Causality methods for non-normal non-linear data, boostrap techniques

  • Application to network performance

 

Selected publications: 

  • H. Hours, E. Biersack, and P. Loiseau. A Causal Approach to the Study of TCP Performance. ACM Transactions on Intelligent Systems and Technology, Special Issue on Causal Discovery and Inference (K. Zhang, J. Li, E. Bareinboim, B. Schölkopf, and J. Pearl, editors), 7(2):25:1--25:25, 2016. [ bib | pdf | http ]

  • H. Hours, E. Biersack, P. Loiseau, A. Finamore, and M. Mellia. A Study of the Impact of DNS Resolvers on CDN Performance Using a Causal Approach. Computer Networks, Special issue on “Traffic and Performance in the Big Data Era”, Volume 109, Part 2, 2016. [ bib | pdf (main text) | pdf (appendix) | http ]

Syndicate

Syndicate content

Data Science