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ECODE (Experimental COgnitive Distributed Engine) FP7 Project Overview

    As part of the Future Internet Research and Experimentation (FIRE) initiative, the ECODE FP7 project designs and experiments machine learning-based control functionality. For this purpose, the project designs, develops, and experiments a distributed machine learning component that augments the capability and functionality of the routing and the forwarding engine of current routers. To evaluate the executability and the performance of the developed machine learning based control functionality, several experiments are conducted at the iLab.t experimental facility, located at IBBT in Ghent, Belgium.

Objectives

    Design and experiment a cognitive routing system that aims at addressing the challenges of the Internet:
    • Security and diagnosability: monitor the path performance by combining adaptive passive and active measurements, and cooperatively detect traffic anomalies (leading to performance decrease) so as to detect intrusions and attacks;
    • Availability/resiliency and accountability: informed path ranking based on metrics, path re-routing to other links in cases of failure, and traffic flows correlation by routers to diagnose and predict performance deviation over time (with respect to profiles), and adapt these profiles so as to maintain an acceptable resource usage;
    • Scalability and quality of the routing system: by detecting events that are detrimental to the routing system dynamics (convergence, stability/ robustness, and stretch), decide and efficiently react to such events.

    This cognitive routing system combines novel networking techniques with on-line, adaptive and distributed machine learning techniques. The resulting system intends to preserve as much as possible original Internet design principles (end-to-end, transparency, etc.).

    Prototype cognitive routing system on XORP open routing emulation platform and validate the machine learning techniques on physical (iLAB) and virtual (e.g., OneLab) experimental facilities.

Methodology

    Our methodology relies on cross-fertilization between the networking and machine-based domains to form a cognitive routing system answering the operational and new Internet challenges. Indeed, they are similar in nature to the conditions traditionally encountered in classical machine learning problems:
    • Nature: the events cannot be well characterized even when examples of such an event are available (inherent complexity in precisely characterizing an event);
    • Relationship: the correlations and trends between events are hidden within large amounts of data that are associated to these events;
    • Environment: the conditions are changing over time (this is particularly the case for the routing environment but also variability of user demands, expectations and behaviours);
    • Quantity: the amount of available data is too large for handling by human intervention;
    • Evolutive: new events are constantly detected/discovered.

Structure

    The project is decomposed into one workpackage dealing with architectural aspects (in particular, the architecture of the machine learning component and the experimental software architecture) and two experimental workpackages. This first experimental workpackage is dedicated to the validation of the machine learning models and algorithms. The second experimental workpackage is dedicated to the evaluation of these algorithms by means of the experimental component.