Traffic Matrix Prediction Based on Deep Learning for Dynamic Traffic Engineering Traffic matrix (TM) is a critical information for network operation and management, especially for traffic engineering (TE). Due to the technical and mercantile problems, real time measurement for TM is difficult in large scale networks. In this paper, we focus on predicting TM for dynamic traffic engineering. We propose several TM prediction methods based on Neural Networks (NN) and predict TM from three perspectives: predict the overall TM directly, predict each origindestination (OD) flow separately and predict the overall TM combined with key element correction. In addition to the prediction accuracy, we evaluate different prediction methods through the performance of TE, as well as the prediction time. We test the proposed methods by real world datasets from Abilene, CERNET and GEANT. The experiment results show that prediction methods based on Recurrent Neural Networks (RNN) can achieve better prediction accuracy than methods leveraging Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN). Predicting each OD flow through RNN models can further improve the prediction accuracy, as well as the performance of TE under the OSPF network scenario, the SDNOSPF hybrid network scenario and the multicommodity flow problem scenario. However, it takes longer prediction time for predicting each OD flow sequence. In contrast, predicting the overall TM combined with key element correction can provide a tradeoff between the TE results and the prediction overhead, which is more appropriate for dynamic TE in the above three scenarios.

A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP The RoundTrip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The endtoend RTT value influences significantly the dynamics and performance of TCP, which is by far the most used communication protocol. Thus, in communication networks, RTT is an important network performance variable. By measuring the traffic at an intermediate node, a network operator or service provider can estimate the RTT and use the estimation to study and troubleshoot the perconnection characteristics and performance. This paper aims at improving the accuracy and timeliness of the RTT estimation, to help network operators improving their analysis. We propose and evaluate a novel deep learningbased model capable of dynamically predicting at realtime the RTT between the sender and receiver with high accuracy based on passive measurements collected at an intermediate node, taking advantage of the commonly used TCP timestamps. We validate extensively our prediction methodology in a controlled experimental testbed and in a realistic scenario on the Google Cloud platform. We show that our model, which is based on classical deep learning algorithms, gives reasonably effective stateoftheart performance results across multiple TCP congestion control variants. We also show that the model works well for transfer learning. Even though the RTT prediction model was trained on an emulated network, it performs well also when applied to a realistic scenario setting, as demonstrated in our experimental evaluation.