Keynote speech 4: Smart multimedia services distribution using Software Defined adaptive cognitive Networks

Current networks have much limitations due to their rigidity, which is given by static configurations mainly based on commands or static scripts. The resource provisioning is less automatic and the efficiency decreases. Moreover, virtualization and cloud are changing radically the traffic patterns of the data center. This is mainly due to the communication between servers, because the applications are split in many virtual machines that must communicate. Software Defined Networks (SDNs) are able to divide the control plane from the data plane, which allow higher programmable, automatic and flexible networks. In SDNs, we do not need to program node by node, but by a centralized manner through software that can be implemented independently of the manufacturer or the model (if they are supporting the same communication protocol). SDNs provide a more open network and allow accessing better to certain intelligent functions, which can contribute higher intelligence to the network operating. These features make SDNs ideal to have a system that is able to adapt with the aim of having higher performance. Cognitive networks use the information gathered from the network, such as observing traffic patterns for different network devices or the used protocols, the behavior of the users and servers, and the additional information that can be taken from the wireless networks (user movement, location, etc.), in order to implement a series of procedures. In order to achieve this goal, artificial intelligence and automatic learning will be used over the available information. This will allow improving a specific objective and achieve higher system performance. This speech will show the steps performed in a cooperative project where we designed and developed a network architecture and the communication protocol, that use the cognitive information taken from the data frames, the users and servers behavior, and the traffic patterns (traffic changes, quality of service parameters, state of the frames, etc.) with the aim of improving the multimedia delivery performance. The designed network is able to self adapt in each case. Network devices gather network parameters and patters that are used by a smart network algorithm to evolve behaviors based on the empirical data. The cognitive adaptive software defined network can be implemented in a wide range of multimedia applications.