Anticipatory optimal network control can be defined as the practice of determining the set of control actions that minimizes a network-wide objective function, so that the consequences of this action are taken in consideration not only locally, on the propagation of flows, but globally, taking into account the user’s routing behaviour. Such an objective function is, in general, defined and optimized in a centralized setting, as knowledge regarding the whole network is needed in order to correctly compute it. This is a strong theoretical framework but, in practice, reaching a level of centralization sufficient to achieve said optimality is very challenging; different organisms control only part of the network, with close to no interaction between each other. Furthermore, even if centralization was possible, it would exhibit several shortcomings, with concerns such as computational speed (centralized optimization of a huge control set with a highly nonlinear objective function), reliability and communication overhead arising.
The main aim of this work is to develop a local, distributed heuristic descent algorithm that, demanding nothing more than for the different control entities to share the same information set, attains network-wide optimality through distributed actions.