Adaptive Shaping For Intelligent Data Center Utilization

The project focuses on edge intelligence for predictive load balancing within an optically switched data center network. Large multi-tenant multi-application data centers tend to run at utilizations that are below 20%. This is due to the network not being able to predict where hotspots will develop. Our aim is to use the NFP to predict where hotspots will happen and pre-emptively shape the network to adapt. We optimize flow placement for a hybrid network implementing an adaptive neural network classifier. We predict elephant flows with high accuracy on anonymized university network traffic. Using the NFP we demonstrate 40Gbps capability.