Search Results for author: Pierre Sens

Found 7 papers, 1 papers with code

DRL-based Slice Placement under Realistic Network Load Conditions

no code implementations27 Sep 2021 José Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence.

reinforcement-learning Reinforcement Learning (RL)

On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement

no code implementations5 Aug 2021 Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works.

Management reinforcement-learning +1

DRL-based Slice Placement Under Non-Stationary Conditions

no code implementations5 Aug 2021 Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process.

Reinforcement Learning (RL)

Controlled Deep Reinforcement Learning for Optimized Slice Placement

no code implementations3 Aug 2021 Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" to solve the problem of Network Slice Placement Optimization.

Network Embedding reinforcement-learning +1

A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

no code implementations14 May 2021 Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens

Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multiobjective Integer Linear Programming (ILP) problem.

reinforcement-learning Reinforcement Learning (RL)

Glycan processing in the Golgi -- optimal information coding and constraints on cisternal number and enzyme specificity

no code implementations18 May 2020 Alkesh Yadav, Quentin Vagne, Pierre Sens, Garud Iyengar, Madan Rao

In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity.

Specificity

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