Internet Traffic Prediction with Distributed Multi-Agent Learning

Abstract

Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches are trained and deployed in a centralized approach, without considering the realistic scenario in which multiple parties are concerned about the prediction process and the prediction model can be trained in a distributed approach. In this study, a distributed multi-agent learning framework is proposed to fill the research gap and predict Internet traffic in a distributed approach, in which each agent trains a base prediction model and the individual models are further aggregated with the cooperative interaction process. In the numerical experiments, two sophisticated deep learning models are chosen as the base prediction model, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). The numerical experiments demonstrate that the GRU model trained with five agents achieves state-of-the-art performance on a real-world Internet traffic dataset collected in a campus backbone network in terms of root mean square error (RMSE) and mean absolute error (MAE).

Drone detection with radio frequency signals and deep learning models

“…[25]. Distributed multi-agent learning [26] and crowd sensing [27] can also be used for effective UAV detection and classification when data are collected using a collaborative approach, and the detection model is trained using a distributed approach.…”

Energy consumption forecasting with deep learning

“…This synergy presents promising avenues for future research exploration: The first research direction involves exploring the application of graphbased deep learning models [24] for energy consumption forecasting, as these models have demonstrated effectiveness in addressing similar problems. The second research direction entails the implementation of distributed learning techniques [25], which would be well-suited for real-world systems, enabling scalable and efficient predictions. The third research direction revolves around the joint forecasting of weather and energy consumption [26], a potentially more effective approach that considers the interplay…”

Insight Into the Architecture and Technology of Smart Identifier Networks

“…Figure 4 shows the evolution of ICN since Cheriton first introduced it in 1999 while solving name-based routing in the Translation Relay Internet Architecture Integrated Active Directory (TRIAD) architecture. When the user needs to obtain certain information, the network retrieves the content according to the information name, and since ICN starts from the information itself, it can implement a customized encryption mechanism for the information content according to the needs [11]. There is a cache mechanism in the ICN node, and the transmission resources are cached according to the node cache selection algorithm, making full use of network storage resources and reducing the problem of repeated transmission of the same content by similar network devices.…”