BEAUMONT, Olivier; LAMBERT, Thomas; MARCHAL, Loris; THOMAS, Bastien

doi:10.1109/IPDPSW.2018.00187

Métadonnées

Licence d’utilisation du document

BEAUMONT, Olivier
Reformulations based algorithms for Combinatorial Optimization [Realopt]

LAMBERT, Thomas
Reformulations based algorithms for Combinatorial Optimization [Realopt]

MARCHAL, Loris
École normale supérieure de Lyon [ENS de Lyon]
Optimisation des ressources : modèles, algorithmes et ordonnancement [ROMA]

Langue

Communication dans un congrès

Ce document a été publié dans

IPDPSW 2018 IEEE International Parallel and Distributed Processing Symposium Workshops, 2018-05-21, Vancouver. p. 1-8

IEEE

Résumé en anglais

In this paper we concentrate on a crucial parameter for efficiency in Big Data and HPC applications: data locality. We focus on the scheduling of a set of independant tasks, each depending on an input file. We assume that each of these input files has been replicated several times and placed in local storage of different nodes of a cluster, similarly of what we can find on HDFS system for example. We consider two optimization problems, related to the two natural metrics: makespan optimization (under the constraint that only local tasks are allowed) and communication optimization (under the constraint of never letting a processor idle in order to optimize makespan). For both problems we investigate the performance of dynamic schedulers, in particular the basic greedy algorithm we can for example find in the default MapReduce scheduler. First we theoretically study its performance, with probabilistic models, and provide a lower bound for communication metric and asymptotic behaviour for both metrics. Second we propose simulations based on traces from a Hadoop cluster to compare the different dynamic schedulers and assess the expected behaviour obtained with the theoretical study.< Réduire