Framework For Traffic Engineering Under Uncertain Traffic Information

Traffic engineering (TE) plays an essential role in deciding routes that effectively use network resources. The TE controller should handle the uncertainty due to the lags and lacks of collected network information. Many previous work partially tackled this uncertainty problem in various aspects e.g. data collection, estimation, prediction, routing with uncertain traffic. However there are few studies about integrating these partial processes to achieve the cooperation. In this paper, we proposed a framework to integrate partial processes in a whole TE process, and formulate it according to the Bayesian approach. In our framework, decision making process considers how the decision affects not only network but also other processes by modeling the behavior of the process as conditional probability. Thus, the cooperation of different processes is expected to be achieved.