Extremal optimization approach applied to initial mapping of distributed java programs

Authors:
Ivanoe De Falco;Eryk Laskowski;Richard Olejnik;Umberto Scafuri;Ernesto Tarantino;Marek Tudruj
Affiliations:
Institute of High Performance Computing and Networking, ICAR, CNR, Naples, Italy;Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland;Computer Science Laboratory, University of Science and Technology of Lille, France;Institute of High Performance Computing and Networking, ICAR, CNR, Naples, Italy;Institute of High Performance Computing and Networking, ICAR, CNR, Naples, Italy;Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland and Polish-Japanese Institute of Information Technology, Warsaw, Poland
Venue:
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Year:
2010

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Abstract

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.