Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Abstract

Today’s Network Operation Centres (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. But this model is getting increasingly expensive and inefficient; hence, the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this article, we investigate whether an autonomous NOC can achieve superintelligence; i.e., recommend or take actions that lead to better results than those achieved by rules designed by human experts. Our investigation is inspired by the superintelligence achieved in computer games recently. Specifically, we build an Action Recommendation Engine using Reinforcement Learning, train it with expert rules, and let it explore actions by itself. We then show that it can learn new and more efficient strategies that outperform expert rules designed by humans. This can be used in the face of network problems to either quickly recommend actions to NOC technicians or autonomously take actions for fast recovery.

Keywords

• Network automation
• network operation center
• reinforcement learning
• self-healing networks