Distributed Java Programs Initial Mapping Based on Extremal Optimization

• Main Authors: Olejnik, Richard, de Falco, Ivanoe, Laskowski, Eryk, Scafuri, Umberto, Tarantino, Ernesto, Tudruj, Marek
• Other Authors: Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Contributions of the Data parallelism to real time (DART), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), LIFL - DART/Émeraude, Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Institute of High Performance Computing and Networking, CNR, Naples, Italy, Institute of High Performance Computing and Networking (ICAR), National Research Council of Italy, Institute of Computer Science Warszawa, Polska Akademia Nauk = Polish Academy of Sciences = Académie polonaise des sciences (PAN), Polish-Japanese Institute of Information Technology (PJIIT), PICS CNRS, Springer-Verlag Berlin Heidelberg, Académie des Sciences de Pologne, CNRS Italie
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2010
• Subjects: distributed systems; program optimization; evolutionary algorithm; LNCS; [INFO.INFO-DC]Computer Science [cs]/Distributed; Parallel; and Cluster Computing [cs.DC]; Iceland
• Online Access: https://hal.science/hal-00834016; https://doi.org/10.1007/978-3-642-28151-8_8

11 pages International audience 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.