  This paper addresses the use of Big Data and machine learning based analytics
to the real-time management of Internet scale Quality-of-Service Route
Optimisation with the help of an overlay network. Based on the collection of
large amounts of data sampled each $2$ minutes over a large number of
source-destinations pairs, we show that intercontinental Internet Protocol (IP)
paths are far from optimal with respect to Quality of Service (QoS) metrics
such as end-to-end round-trip delay. We therefore develop a machine learning
based scheme that exploits large scale data collected from communicating node
pairs in a multi-hop overlay network that uses IP between the overlay nodes
themselves, to select paths that provide substantially better QoS than IP. The
approach inspired from Cognitive Packet Network protocol, uses Random Neural
Networks with Reinforcement Learning based on the massive data that is
collected, to select intermediate overlay hops resulting in significantly
better QoS than IP itself. The routing scheme is illustrated on a $20$-node
intercontinental overlay network that collects close to $2\times 10^6$
measurements per week, and makes scalable distributed routing decisions.
Experimental results show that this approach improves QoS significantly and
efficiently in a scalable manner.
