The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective
Recently, deep learning, an emerging machine learning technique, is garnering a lot of
research attention in several computer science areas. However, to the best of our …
research attention in several computer science areas. However, to the best of our …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
On removing routing protocol from future wireless networks: A real-time deep learning approach for intelligent traffic control
Recently, deep learning has appeared as a breakthrough machine learning technique for
various areas in computer science as well as other disciplines. However, the application of …
various areas in computer science as well as other disciplines. However, the application of …
Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning
Recent years, Software Defined Routers (SDRs)(programmable routers) have emerged as a
viable solution to provide a cost-effective packet processing platform with easy extensibility …
viable solution to provide a cost-effective packet processing platform with easy extensibility …
Is machine learning ready for traffic engineering optimization?
G Bernárdez, J Suárez-Varela, A López… - 2021 IEEE 29th …, 2021 - ieeexplore.ieee.org
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze
whether modern Machine Learning (ML) methods are ready to be used for TE optimization …
whether modern Machine Learning (ML) methods are ready to be used for TE optimization …
Predicting network flow characteristics using deep learning and real-world network traffic
We present a processing pipeline for flow-based traffic classification using a machine
learning component leveraging Deep Neural Networks (DNNs). The system is trained to …
learning component leveraging Deep Neural Networks (DNNs). The system is trained to …
[HTML][HTML] Intelligent traffic management in next-generation networks
The recent development of smart devices has lead to an explosion in data generation and
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …
heterogeneity. Hence, current networks should evolve to become more intelligent, efficient …
Deep reinforcement learning for router selection in network with heavy traffic
The rapid development of wireless communications brings a tremendous increase in the
amount number of data streams and poses significant challenges to the traditional routing …
amount number of data streams and poses significant challenges to the traditional routing …