A Location-Aware Strategy for Agents Negotiating Load-balancing

Quentin Baert (1, 2, 3) , Anne-Cécile Caron (1, 2, 3) , Maxime Morge (2, 3, 1) , Jean-Christophe Routier (2, 3, 1) , Kostas Stathis (4)

1 SMAC - Systèmes Multi-Agents et Comportements
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 Université de Lille
4 Department of Computer Science [Royal Holloway]

We study a novel location-aware strategy for distributed systems where cooperating agents perform the load-balancing. The strategy allows agents to identify opportunities within a current unbalanced allocation , which in turn triggers concurrent and one-to-many negotiations amongst agents to locally reallocate some tasks. The tasks are reallocated according to the proximity of the resources and they are performed in accordance with the capabilities of the nodes in which agents are situated. This dynamic and ongoing negotiation process takes place concurrently with the task execution and so the task allocation process is adaptive to disruptions (task consumption, slowing down nodes). We evaluate the strategy in a multi-agent deployment of the MapReduce design pattern for processing large datasets. Empirical results demonstrate that our strategy significantly improves the overall runtime of the data processing.

Domaines: Système multi-agents [cs.MA]; Modélisation et simulation; Intelligence artificielle [cs.AI]

Quentin Baert, Anne-Cécile Caron, Maxime Morge, Jean-Christophe Routier, Kostas Stathis. A Location-Aware Strategy for Agents Negotiating Load-balancing. 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨10.1109/ICTAI.2019.00098⟩. ⟨hal-02344457⟩