Section: New Results

Learning for an efficient and dynamic management of network resources and services

Participants : Dana Marinca, Pascale Minet.

To guarantee an efficient and dynamic management of network resources and services we intend to use a powerful mathematical tool: prediction and learning from prediction. Prediction will be concerned with guessing the short-term, average-term and long-term evolution of network or network components state, based on knowledge about the past elements and/or other available information. Basically, the prediction problem could be formulated as follows: a forecaster observes the values of one or several metrics giving indications about the network state (generally speaking the network represents the environment). At each time $t$, before the environment reveals the new metric values, the forecaster predicts the new values based on previous observations. Contrary to classical methods where the environment evolution is characterized by stochastic process, we suppose that the environment evolution follows an unspecified mechanism, which could be deterministic, stochastic, or even adaptive to a given behavior. The prediction process should adapt to unpredictable network state changes due to its non-stationary nature. To properly address the adaptivity challenge, a special type of forecasters is used: the experts. These experts analyse the previous environment values, apply their own computation and make their own prediction. The experts predictions are given to the forecaster before the next environment values are revealed. The forecaster can then make its own prediction depending on the experts' "advice". The risk of a prediction may be defined as the value of a loss function measuring the discrepancy between the predicted value and the real environment value. The principal notion to optimize the behavior of the forecasters is the regret, seen as a difference between the forecaster's accumulated loss and that of each expert. To optimize the prediction process means to construct a forecasting strategy that guarantees a small loss with respect to defined experts. Adaptability of the forecaster is reflected in the manner in which it is able to follow the better expert according to the context. We intend to use and improve this prediction technique to design dynamically adaptive regret matching algorithms that will be applied to dynamically manage the resources in wireless networks, especially in sensor networks. These algorithms will allow the network to choose an optimal behavior, otherwise called a correlated equilibrium, from a defined behaviors' set. This behavior will be able to evolve in time to adapt to the network context evolution. We will focus mainly but not exclusively on applications like: the choice of communication channels depending on the predicted quality of transmission, energy efficiency, network nodes deployment, efficient routing, and intelligent switching between available technologies in a multi-technology context.