The goal of the project is to design an architecture and self-learning algorithms that can optimize and guarantee deterministic QoS of individual traffic flows whilst maximizing network resource efficiency. The general QoS architecture for end-to-end communication of heterogeneous types of traffic will include routing and scheduling for each traffic type and admission control possibly with QoS renegotiations and with shaping and spacing.