Analysis of a batch strategy for a Master-Worker adaptive selection algorithm framework

Christopher Jankee (1) , Sébastien Verel (1) , Cyril Fonlupt (1) , Bilel Derbel (2, 3)

1 LISIC - Laboratoire d'Informatique Signal et Image de la Côte d'Opale
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 DOLPHIN - Parallel Cooperative Multi-criteria Optimization

We look into the design of a parallel adaptive algorithm embedded in a master-slave scheme. The adaptive algorithm under study selects online and in parallel for each slave-node one algorithm from a portfolio. Indeed, many open questions still arise when designing an online distributed strategy that attributes optimally algorithms to distribute resources. We suggest to analyze the relevance of existing sequential adaptive strategies related to multi-armed bandits to the master-slave distributed framework. In particular, the comprehensive experimental study focuses on the gain of computing power, the adaptive ability of selection strategies, and the communication cost of the parallel system. In fact, we propose an adaptive batch mode in which a sequence of algorithms is submitted to each slave computing node to face a possibly high communication cost.

Mots clés: Master-Worker Architecture; Adaptive Selection Strategy

Domaines: Recherche opérationnelle [math.OC]

Christopher Jankee, Sébastien Verel, Cyril Fonlupt, Bilel Derbel. Analysis of a batch strategy for a Master-Worker adaptive selection algorithm framework. 9th International Joint Conference on Computational Intelligence (IJCCI 2017), Nov 2017, Madère, Portugal. pp.313-320, ⟨10.5220/0006504203130320⟩. ⟨hal-01643337⟩