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 …
Magnneto: A graph neural network-based multi-agent system for traffic engineering
G Bernárdez, J Suárez-Varela, A López… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of
network optimization tasks. As such, many efforts have been made to produce ML-based …
network optimization tasks. As such, many efforts have been made to produce ML-based …
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 …
A multi-agent reinforcement learning perspective on distributed traffic engineering
N Geng, T Lan, V Aggarwal, Y Yang… - 2020 IEEE 28th …, 2020 - ieeexplore.ieee.org
Traffic engineering (TE) in multi-region networks is a challenging problem due to the
requirement that each region must independently compute its routing decisions based on …
requirement that each region must independently compute its routing decisions based on …
Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for
commercial optimization engines to efficiently solve network traffic engineering (TE) …
commercial optimization engines to efficiently solve network traffic engineering (TE) …
Delay-optimal traffic engineering through multi-agent reinforcement learning
P Pinyoanuntapong, M Lee… - IEEE INFOCOM 2019 …, 2019 - ieeexplore.ieee.org
Traffic engineering is one of the most important methods of optimizing network performance
by designing optimal forwarding and routing rules to meet the quality of service (QoS) …
by designing optimal forwarding and routing rules to meet the quality of service (QoS) …
A scalable deep reinforcement learning approach for traffic engineering based on link control
P Sun, J Lan, J Li, J Zhang, Y Hu… - IEEE Communications …, 2020 - ieeexplore.ieee.org
As modern communication networks are growing more complicated and dynamic, designing
a good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
a good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
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 …
A deep reinforcement learning perspective on internet congestion control
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 …
Repetita: Repeatable experiments for performance evaluation of traffic-engineering algorithms
In this paper, we propose a pragmatic approach to improve reproducibility of experimental
analyses of traffic engineering (TE) algorithms, whose implementation, evaluation and …
analyses of traffic engineering (TE) algorithms, whose implementation, evaluation and …