A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
that solve problems and enable automation in diverse domains. Primarily, this is due to the …
In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
Active queue management: A survey
R Adams - IEEE communications surveys & tutorials, 2012 - ieeexplore.ieee.org
Since its formal introduction to IP networks in 1993 as a viable complementary approach for
congestion control, there has been a steady stream of research output with respect to Active …
congestion control, there has been a steady stream of research output with respect to Active …
A deterministic improved Q-learning for path planning of a mobile robot
A Konar, IG Chakraborty, SJ Singh… - … on Systems, Man …, 2013 - ieeexplore.ieee.org
This paper provides a new deterministic Q-learning with a presumed knowledge about the
distance from the current state to both the next state and the goal. This knowledge is …
distance from the current state to both the next state and the goal. This knowledge is …
When machine learning meets congestion control: A survey and comparison
Abstract Machine learning has seen a significant surge and uptake across many diverse
applications. The high flexibility, adaptability, and computing capabilities it provides extend …
applications. The high flexibility, adaptability, and computing capabilities it provides extend …
Multi-criteria evaluation and benchmarking for active queue management methods: Open issues, challenges and recommended pathway solutions
The evaluation and benchmarking processes of active queue management (AQM) methods
are complicated and challenging. Several evaluation criteria/metrics must be considered …
are complicated and challenging. Several evaluation criteria/metrics must be considered …
Machine learning controller for data rate management in science DMZ networks
This article presents a Machine Learning Controller (MLC) supported by a P4 switch for
improving rate control in non-dedicated Science Demilitarized Zone (Science DMZ) …
improving rate control in non-dedicated Science Demilitarized Zone (Science DMZ) …
QRED: A Q-learning-based active queue management scheme
Y Su, L Huang, C Feng - Journal of Internet Technology, 2018 - jit.ndhu.edu.tw
Abstract The Active Queue Management (AQM) algorithm is one of most important research
fields in network congestion control. To adjust the maximum dropping probability (maxp) …
fields in network congestion control. To adjust the maximum dropping probability (maxp) …
[PDF][PDF] Controlling delay at the router buffer using modified random early detection
AA Abu-Shareha - International Journal of Computer Networks & …, 2019 - researchgate.net
ABSTRACT Active Queue Management (AQM) methods are used to manage the buffer of
the network routers and avoid the problems caused by network congestion, especially …
the network routers and avoid the problems caused by network congestion, especially …
[PDF][PDF] An improved Q-learning algorithm for path-planning of a mobile robot
Classical Q-learning requires huge computations to attain convergence and a large storage
to save the Q-values for all possible actions in a given state. This paper proposes an …
to save the Q-values for all possible actions in a given state. This paper proposes an …