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View article: Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks Open
Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing. However, existing RL approaches often suffer from lack of interpretability and difficulty in extracting controller equations. In…
View article: Safe Load Balancing in Software-Defined-Networking
Safe Load Balancing in Software-Defined-Networking Open
High performance, reliability and safety are crucial properties of any Software-Defined-Networking (SDN) system. Although the use of Deep Reinforcement Learning (DRL) algorithms has been widely studied to improve performance, their practic…
View article: Deep Reinforcement Learning for Smart Queue Management
Deep Reinforcement Learning for Smart Queue Management Open
With the goal of meeting the stringent throughput and delay requirements of classified network flows, we propose a Deep Q-learning Network (DQN) for optimal weight selection in an active queue management system based on Weighted Fair Queui…
View article: Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning Open
Deep Reinforcement Learning (DRL) algorithms have recently made significant strides in improving network performance. Nonetheless, their practical use is still limited in the absence of safe exploration and safe decision-making. In the con…
View article: Graph Convolutional Reinforcement Learning for Load Balancing and Smart Queuing
Graph Convolutional Reinforcement Learning for Load Balancing and Smart Queuing Open
International audience
View article: AMAC: Attention-based Multi-Agent Cooperation for Smart Load Balancing
AMAC: Attention-based Multi-Agent Cooperation for Smart Load Balancing Open
International audience
View article: Global QoS Policy Optimization in SD-WAN
Global QoS Policy Optimization in SD-WAN Open
In modern SD-WAN networks, a global controller is able to steer traffic on different paths based on application requirements and global intents. However, existing solutions cannot dynamically tune the way bandwidth is shared between flows …
View article: Routing and QoS Policy Optimization in SD-WAN
Routing and QoS Policy Optimization in SD-WAN Open
In modern SD-WAN networks, a global controller continuously optimizes application and user intents by selecting the proper routing policies for each application. Nevertheless, the competition between flows can still occur at each overlay l…
View article: Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents Open
In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We c…
View article: Clinic and Paraclinic of Middle Ear Tubeculosis in the National ENT Hospital of Hanoi-Vietnam in the period from 08/2018 to 08/2019
Clinic and Paraclinic of Middle Ear Tubeculosis in the National ENT Hospital of Hanoi-Vietnam in the period from 08/2018 to 08/2019 Open
Background Extrapulmonary tuberculosis is currently more and more in Vietnam, especially in the head and neck area with about 4-6%. Due to its very atypical clinic, middle ear tuberculosis is diagnosed very late, especially in the case not…
View article: On the Use of Graph Neural Networks for Virtual Network Embedding
On the Use of Graph Neural Networks for Virtual Network Embedding Open
International audience
View article: On Using Deep Reinforcement Learning for VNF Forwarding Graphs Placement
On Using Deep Reinforcement Learning for VNF Forwarding Graphs Placement Open
International audience
View article: Evolutionary Actor-Multi-Critic Model for VNF-FG Embedding
Evolutionary Actor-Multi-Critic Model for VNF-FG Embedding Open
International audience
View article: A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding
A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding Open
International audience
View article: Virtual network function–forwarding graph embedding: A genetic algorithm approach
Virtual network function–forwarding graph embedding: A genetic algorithm approach Open
Summary Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph…
View article: Multi-domain non-cooperative VNF-FG embedding: A deep reinforcement learning approach
Multi-domain non-cooperative VNF-FG embedding: A deep reinforcement learning approach Open
International audience
View article: Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking
Deep Reinforcement Learning based QoS-aware Routing in Knowledge-defined networking Open
International audience
View article: Multi-objective multi-constrained QoS Routing in large-scale networks: A genetic algorithm approach
Multi-objective multi-constrained QoS Routing in large-scale networks: A genetic algorithm approach Open
International audience
View article: Algorithms and optimization for quality of experience aware routing in wireless networks : from centralized to decentralized solutions
Algorithms and optimization for quality of experience aware routing in wireless networks : from centralized to decentralized solutions Open
WMNs comprise nodes that are able to receive and forward the data to other destinations in the networks. Consequently, WMNs are able to dynamically self-organize and self-configure [5]. Each node itself creates and maintains the connectivi…