Abstract:
As emerging network technologies and softwareization render networks more flexible, the question arises of how to exploit these flexibilities for optimization. Given the complexity of the involved network protocols and the context in which networks are operating, such optimizations are increasingly difficult to perform. An interesting vision in this regard are “self-driving” networks: networks which measure, analyze and control themselves in an automated manner, reacting to changes in the environment (e.g., demand), while exploiting existing flexibilities to optimize themselves. A fundamental challenge faced by any (self-)optimizing network concerns the limited knowledge about future changes in the demand and environment in which the network is operating. Indeed, given that reconfigurations entail resource costs and may take time, an “optimal” network configuration for the current demand and environment may not necessarily be optimal also in the near future. Thus, it is desirable that (self-)optimizations also prepare the network for possibly unexpected events. During the analysis of measured data, self-driving networks must thus be able to extract useful information that allows them to reason about the current state they are in and predict likely future developments. This talk discusses how networks can be equipped with the capability to reason and actively reconfigure themselves in the absence of external requests. This talk shows how Stochastic Block Models, a specific class of Directed Graphical Models, allows networks to answer a wide range of different questions. Then, this talk discusses the active reconfiguration of networks through an information theoretic quantity called empowerment.
