ARTIC_1_Ukraine

By 2021, cloud IP traffic will be the most part of an Internet traffic that complexifies with an increasing devices diversity and traffic dynamicity. A proposal framed at the cloud to face this situation is the Knowledge Defined Networking (KDN), where Machine Learning (ML) and Artificial Intelligence (AI) are combined with SDN/NFV and network monitoring to collect data, transform them into knowledge (e.g. models) via ML, and take decisions with this knowledge. Under this paradigm, we aim to design a unified AI-based framework able to learn new efficient cloud network control algorithms. This framework will integrate seamlessly data-driven control (based on ML tools) and model-driven control (based on optimization models), addressing scalability and optimality issues of the cloud control. To do that, we intend to apply two promising AI tools: Deep Learning (DL); and, Reinforcement Learning (RL). In the project, a Deep Learning Artificial Neural Network (ANN) will be used to transform the original input data representations (in our case, the cloud network state) into a low dimensional space where the network structural information and network properties are maximally preserved, and used them to solve in a more tractable way the optimal control problem. RL will be applied to learn the optimal control by interacting with the environment (in our case, the Cloud network).These interactions can be used to guide the learning of the weights of the deep ANN. The result is that the RL algorithm (acting as control loop) will solve more easily the control problem using as input this more compacted and lower dimensional representations found by the deep neural network. The main novelty of our approach is that we state that, for network control problems, the deep ANN should not be implemented using the same deep layer architectures used in computer vision (the so-called convolutional layers), but using a different kind (the so-called novel graph embedding architectures) better fitted to the graph nature of the network problems. Then, we propose to use the graph embedding layers as deep layers to solve cloud network control problems, namely the dynamic allocation of service chains composed by network virtualised functions. Starting from the case where the network service is unicast, we will move later to the multicast case, since video delivery, the classical multicast service, is the Internet killer application. Finally, we will implement a KDN proof-of-concept tested where our Deep Reinforcement Learning control will send via the northbound interface the control decisions to a an SDN controller, that, in its turn, will instruct an emulated SDN network.