A Machine Learning Approach to Routing

Abstract

Can ideas and techniques from machine learning be leveraged to automatically generate "good" routing configurations? We investigate the power of data-driven routing protocols. Our results suggest that applying ideas and techniques from deep reinforcement learning to this context yields high performance, motivating further research along these lines.

References in corpus (2)

• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
• PCC: Re-architecting Congestion Control for Consistent High Performance

Cited by in corpus (1)

• Optimizing Redundancy Levels in Master-Worker Compute Clusters for Straggler Mitigation