Experience-driven networking: A deep reinforcement learning based approach
Modern communication networks have become very complicated and highly dynamic, which
makes them hard to model, predict and control. In this paper, we develop a novel experience …
makes them hard to model, predict and control. In this paper, we develop a novel experience …
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 …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Feature engineering for deep reinforcement learning based routing
J Suárez-Varela, A Mestres, J Yu… - ICC 2019-2019 IEEE …, 2019 - ieeexplore.ieee.org
Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a
dramatic improvement in decision-making and automated control problems. As a result, we …
dramatic improvement in decision-making and automated control problems. As a result, we …
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 …
ScaleDRL: A scalable deep reinforcement learning approach for traffic engineering in SDN with pinning control
As modern communication networks become more complicated and dynamic, designing a
good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
Deep reinforcement learning for multimedia traffic control in software defined networking
Software Defined Networking (SDN) is a promising paradigm to provide centralized traffic
control. Multimedia traffic control based on SDN is crucial but challenging for Quality of …
control. Multimedia traffic control based on SDN is crucial but challenging for Quality of …
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 …
Toward packet routing with fully distributed multiagent deep reinforcement learning
Packet routing is one of the fundamental problems in computer networks in which a router
determines the next-hop of each packet in the queue to get it as quickly as possible to its …
determines the next-hop of each packet in the queue to get it as quickly as possible to its …
Internet congestion control via deep reinforcement learning
We present and investigate a novel and timely application domain for deep reinforcement
learning (RL): Internet congestion control. Congestion control is the core networking task of …
learning (RL): Internet congestion control. Congestion control is the core networking task of …