Server Traffic Prediction Using Machine Learning for Optical Circuit Switching Scheduling

This work presents the performance analysis of nonlinear autoregressive neural networks applied in server traffic prediction and dedicated to hybrid intra-data center networks. This technique offers a high potential for accurate forecasting of heavy traffic streams, which allows for an a priori scheduling and allocation of optical switching circuits for offloading of intensive flows in data center applications. Just as with the artificial data sets carrying the machine-generated traffic characteristics used in our previous work, the nonlinear autoregressive neural networks show a high performance of the prediction mechanism applied to real server traffic.