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.

流量预测和历史数据的利用对于智能和高效的资源管理至关重要，特别是在服务于广泛应用的光数据中心网络（ODCN）中。在这项研究中，我们通过利用应用程序特定数据和需求的精确或可预测知识（例如等待时间、带宽、流量历史记录和延迟）来研究 ODCN 中流量聚合的挑战。由于 ODCN 处理各种流量（包括长/大象和短/小鼠），因此我们采用机器学习 (ML) 来预测随时间变化的流量和连接阻塞。为了改善空间移动光网络的能源利用和资源分配，本研究提出了一种基于机器学习的网络流量分析新方法。这里，我们利用网络监控来为资源分配决策提供信息，目标是使用短期空间复用多层强化学习来降低流量水平。然后，通过使用动态梯度下降分割复用来优化能量。实验中使用了各种指标，包括准确性、NSE（归一化平方误差）、验证损失、平均误差和带宽阻塞概率。最后，使用原对偶内点方法，根据预测结果（包括每个切片的流量和用户负载的分布）研究每个切片应具有多少权重。通过使用动态梯度下降分割复用来优化能量。实验中使用了各种指标，包括准确性、NSE（归一化平方误差）、验证损失、平均误差和带宽阻塞概率。最后，使用原对偶内点方法，根据预测结果（包括每个切片的流量和用户负载的分布）研究每个切片应具有多少权重。通过使用动态梯度下降分割复用来优化能量。实验中使用了各种指标，包括准确性、NSE（归一化平方误差）、验证损失、平均误差和带宽阻塞概率。最后，使用原对偶内点方法，根据预测结果（包括每个切片的流量和用户负载的分布）研究每个切片应具有多少权重。