Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs

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

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1007/978-3-642-15277-1_18

Title :

Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs

Author(s) :

de Falco, Ivanoe [Auteur]
Institute of High Performance Computing and Networking [ICAR]
Laskowski, Eryk [Auteur]
Institute of Computer Science [Warszawa]
Olejnik, Richard [Auteur]

Contributions of the Data parallelism to real time [DART]
LIFL - DART/Émeraude
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Scafuri, Umberto [Auteur]
Institute of High Performance Computing and Networking [ICAR]
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]

Journal title :

Lecture Notes in Computer Science
Euro-Par 2010 - Parallel Processing

Pages :

180-191

Publisher :

Springer

Publication date :

2010-09-03

ISSN :

0302-9743

English keyword(s) :

distributed systems
program optimization
evolutionary algorithms

HAL domain(s) :

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

English abstract : [en]

An extremal optimization algorithm for initial Java program placement on clusters of Java Virtual Machines (JVMs) is presented. JVMs are implemented on multicore processors working under the ProActive Java execution ...
Show more >An extremal optimization algorithm for initial Java program placement on clusters of Java Virtual Machines (JVMs) is presented. JVMs are implemented on multicore processors working under the ProActive Java execution framework. Java programs are represented as Directed Acyclic Graphs in which tasks correspond to methods of distributed active Java objects that communicate using a RMI mechanism. The presented probabilistic extremal optimization approach is based on the local fitness function composed of two sub-functions in which elimination of delays of task execution after reception of required data and the imbalance of tasks execution in processors are used as heuristics for improvements of extremal optimization solutions. The evolution of an extremal optimization solution is governed by task clustering supported by identification of the dominant path in the graph. The applied task mapping is based on dynamic measurements of current loads of JVMs and inter-JVM communication link bandwidth. The JVM loads are approximated by observation of the average idle time that threads report to the OS. The current link bandwidth is determined by observation of the performed average number of RMI calls per second.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :