Towards Superintelligence-Driven Autonomous Network Operation Centers Using Reinforcement Learning

Abstract

Today's Network Operation Centers (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network's health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today's networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors' technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this work, we investigate whether such decisions can be taken by Artificial Intelligence instead of collective human intelligence, specifically by Deep-Reinforcement Learning (DRL), which has been shown in computer games to outperform humans. We build an Action Recommendation Engine (ARE) based on RL, train it with expert rules or by letting it explore outcomes by itself, and show that it can learn new and more efficient strategies that outperform expert rules designed by humans by as much as 25%.