Network traffic reduction with spatially flexible optical networks using machine learning techniques

Traffic forecasting and the utilisation of historical data are essential for intelligent and efficient resource management, particularly in optical data centre networks (ODCNs) that serve a wide range of applications. In this research, we investigate the challenge of traffic aggregation in ODCNs by making use of exact or predictable knowledge of application -certain data and demands, such as waiting time, bandwidth, traffic history, and latency. Since ODCNs process a wide range of flows (including long/elephant and short/mice), we employ machine learning (ML) to foresee time -varying traffic and connection blockage. In order to improve energy use and resource distribution in spatially mobile optical networks, this research proposes a novel method of network traffic analysis based on machine learning. Here, we leverage network monitoring to inform resource allocation decisions, with the goal of decreasing traffic levels using short-term space multiplexing multitier reinforcement learning. Then, the energy is optimised by using dynamic gradient descent division multiplexing. Various metrics, including accuracy, NSE (normalised square error), validation loss, mean average error, and probability of bandwidth blockage, are used in the experiment. Finally, using the primal-dual interior -point approach, we investigate how much weight each slice should have depending on the predicted results, which include the traffic of each slice and the distribution of user load.