The Cognitive Packet Network
for QoS based Routing
ABSTRACT: The Cognitive Packet Network (CPN) is an experimental network routing protocol which uses specific Quality of Service (QoS) objectives incorporated in a Goal Function, together with network measurement by Smart Packets (SPs). It updates neural network based
Oracles in routers using Reinforcement Learning, in order to dynamically select network paths so that end users can convey their payload traffic with a performance that matches the Goal as closely as possible. The Goal can include conventional QoS metrics such as delay and loss, as well as Real-Time objectives, as well as newer metrics of interest including Energy Consumption and Security. Payload traffic is forwarded using source or segment routing, selected through the reinforcement learning approach, while SPs conduct their exploration using a node by node process by seeking the best direction from each Oracle. CPN has been implemented in various contexts: on 10-40 node test-beds, on an intercontinental scale as an overlay network, within SDN routers, and as a means to convey task requests over the Internet
to Cloud servers. Our presentation will detail the CPN algorithm and the Random Neural Networks that are used to implement the Oracles. We will also present relate experimental measurements and results.
SHORT BIOGRAPHY: Erol Gelenbe is a Fellow of ACM, IEEE, the Institution of Engineering and Technology (IET) and the Royal Statistical Society. Also Fellow of the National Academy of Technologies of France, and the Science Academies of Belgium, Hungary, Poland and Turkey, he is Professor in the Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences. Previously he served as the Dennis Gabor Professor of Electrical and Electronic Engineering at Imperial College, also the Nello L. Teer Professor of Electrical and Computer Engineering at Duke University, and the University Chair Professor and Director of Electrical Engineering and Computer Science at the University of Central Florida. Gelenbe invented trailblazing
mathematical models including the G (Gelenbe)-Network and Random Neural Network (RNN), that allow for performance evaluation and analysis of computer systems and networks. Gelenbe’s fundamental contributions in these areas have been instrumental in allowing networks to operate seamlessly without overloading. Along with colleagues, he is credited with patents for an early architecture that allowed voices and images to travel over multi-hop and multi-path computer and communications networks. His recent research interests include Software Defined Networks (SDNs), energy savings in information and computing technology (ICT), security in networks, and reinforcement and deep learning within neural networks. He is also a recipient of the 2008 ACM SIGMETRICS Achievement Award, given annually to an individual who has made long-lasting influential contributions
to the analysis and evaluation of computer and communication system performance, and several other awards, including the Grand Prix France Telecom 1996 of the French Academy of Sciences.