A Multi-Agent Negotiation Strategy for ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
A Multi-Agent Negotiation Strategy for Reducing the Flowtime
Auteur(s) :
Beauprez, Ellie [Auteur]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Caron, Anne-Cecile [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Systèmes Multi-Agents et Comportements [SMAC]
MORGE, Maxime [Auteur]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Routier, Jean-Christophe [Auteur]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Caron, Anne-Cecile [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Systèmes Multi-Agents et Comportements [SMAC]
MORGE, Maxime [Auteur]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Routier, Jean-Christophe [Auteur]
Systèmes Multi-Agents et Comportements [SMAC]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
13th International Conference on Agents and Artificial Intelligence (ICAART)
Ville :
Online streaming
Pays :
Portugal
Date de début de la manifestation scientifique :
2021-02-04
Titre de l’ouvrage :
13th International Conference on Agents and Artificial Intelligence
Titre de la revue :
13th International Conference on Agents and Artificial Intelligence (ICAART)
Éditeur :
INSTICC Press
Date de publication :
2021
Mot(s)-clé(s) en anglais :
Multi-Agent Systems
Distributed Problem Solving
Negotiation and Interaction Protocols
Distributed Problem Solving
Negotiation and Interaction Protocols
Discipline(s) HAL :
Informatique [cs]/Système multi-agents [cs.MA]
Informatique [cs]/Modélisation et simulation
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Modélisation et simulation
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. In this context, we propose a novel strategy based on cooperative agents ...
Lire la suite >In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. In this context, we propose a novel strategy based on cooperative agents used to optimise the rescheduling of tasks for multiple jobs submitted by users in order to be executed as soon as possible. It allows an agent to determine locally the next task to process and the next task to delegate according to its knowledge, its own belief base and its peer modelling. The novelty of our strategy lies in the ability of agents to identify opportunities and limiting factor agents, and afterwards to reallocate some of the tasks. Our contribution is that, thanks to concurrent bilateral negotiations, tasks are continuously reallocated according to the local perception and the peer modelling of agents. In order to evaluate the responsiveness of our approach, we implement a prototype testbed and our experimentation reveals that our strategy reaches a flowtime which is close to the one reached by the classical heuristic approach and significantly reduces the rescheduling time.Lire moins >
Lire la suite >In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. In this context, we propose a novel strategy based on cooperative agents used to optimise the rescheduling of tasks for multiple jobs submitted by users in order to be executed as soon as possible. It allows an agent to determine locally the next task to process and the next task to delegate according to its knowledge, its own belief base and its peer modelling. The novelty of our strategy lies in the ability of agents to identify opportunities and limiting factor agents, and afterwards to reallocate some of the tasks. Our contribution is that, thanks to concurrent bilateral negotiations, tasks are continuously reallocated according to the local perception and the peer modelling of agents. In order to evaluate the responsiveness of our approach, we implement a prototype testbed and our experimentation reveals that our strategy reaches a flowtime which is close to the one reached by the classical heuristic approach and significantly reduces the rescheduling time.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Commentaire :
2021
Collections :
Source :
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- https://doi.org/10.5220/0010226000580068
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- https://doi.org/10.5220/0010226000580068
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