Collaborative strategy learning for distributed network self-configuring

Self-configuration is one of the most important functions of autonomic networks because it determines optimal use of resources during network´s operation. However, this task is very complex as it must be performed according to service contracts between users and operators, network´s infrastructure and workload. Knowledge Plane is a recently proposed concept to address this complexity by using cognitive tools (learning and reasoning). In this paper, we propose a Knowledge Plane including a distributed and collaborative machine learning method based on inductive logic programming (ILP). The main objective is to achieve distributed self-configuring by learning collaboratively best configuration strategies. We apply it in a practical context (DiffServ) and evaluate effects of this proposal on network´s performances and occupation rate.