Extremal Optimization Applied to Task Scheduling of Distributed Java Programs

Olejnik, Richard; de Falco, Ivanoe; Laskowski, Eryk; Scafuri, Umberto; Tarantino, Ernesto; Tudruj, Marek

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-642-20520-0_7

Titre :

Extremal Optimization Applied to Task Scheduling of Distributed Java Programs

Auteur(s) :

Olejnik, Richard [Auteur correspondant]

Contributions of the Data parallelism to real time [DART]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
de Falco, Ivanoe [Auteur]
Laskowski, Eryk [Auteur]
Institute of Computer Science [Warszawa]
Scafuri, Umberto [Auteur]
Tarantino, Ernesto [Auteur]
Institute of High Performance Computing and Networking [ICAR]
Tudruj, Marek [Auteur]
Polish-Japanese Institute of Information Technology [PJIIT]
Institute of Computer Science [Warszawa]

Éditeur(s) ou directeur(s) scientifique(s) :

Springer-Verlag Berlin Heidelberg

Titre de la manifestation scientifique :

EvoApplications 2011

Ville :

Turin

Pays :

Italie

Date de début de la manifestation scientifique :

2013-04-27

Titre de l’ouvrage :

Applications of Evolutionary Computation

Titre de la revue :

Lecture Notes in Computer Science

Éditeur :

Springer-Verlag Berlin Heidelberg

Date de publication :

2011

Mot(s)-clé(s) en anglais :

distributed systems
scheduling
evolutionary algorithms

Discipline(s) HAL :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

Résumé en anglais : [en]

The paper presents new Java programs scheduling algorithms for execution on clusters of Java Virtual Machines (JVMs), which involve extremal optimization (EO) combined with task clustering. Two new scheduling algorithms ...
Lire la suite >The paper presents new Java programs scheduling algorithms for execution on clusters of Java Virtual Machines (JVMs), which involve extremal optimization (EO) combined with task clustering. Two new scheduling algorithms are presented and compared. The first employs task clustering to reduce an initial program graph and then applies extremal optimization to schedule the reduced program graph to system resources. The second algorithm applies task clustering only to find an initial solution which is next improved by the EO algorithm working on the initial program graph. Both algorithms are also compared to an EO algorithm which does not use the clustering approach.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :