The Experts below are selected from a list of 7224 Experts worldwide ranked by ideXlab platform
Marek Tudruj - One of the best experts on this subject based on the ideXlab platform.
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Extremal Optimization Applied to Task Scheduling of Distributed Java Programs
Lecture Notes in Computer Science, 2013Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs
Lecture Notes in Computer Science, 2010Co-Authors: Ivanoe De Falco, Richard Olejnik, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Distributed Java Programs Initial Mapping Based on Extremal Optimization
Lecture Notes in Computer Science, 2010Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Data Mining on Desktop Grid Platforms
Lecture Notes in Computer Science, 2008Co-Authors: Valerie Fiolet, Richard Olejnik, Eryk Laskowski, Marek Tudruj, Łukasz Masko, Bernard TourselAbstract:Very large data volumes and high computation costs in data mining applications justify the use for them of Grid-level massive parallelism. The paper concerns Grid-oriented implementation of the DisDaMin (Distributed Data Mining) project, which proposes distributed knowledge discovery through parallelization of data mining tasks. DisDaMin solves data mining problems by using new distributed algorithms based on special clusterized data decomposition and asynchronous task processing, which match the Grid computing features. The DisDaMin algorithms are embedded inside the DG-ADAJ (Desktop-Grid Adaptative Application in Java) system, which is a middleware platform for Desktop Grid. It provides adaptive control of distributed applications written in Java for Grid or Desktop Grid. It allows an optimized distribution of applications on clusters of Java Virtual Machines, monitoring of application execution and dynamic on-line balancing of processing and communication. Simulations were performed to prove the efficiency of the proposed mechanisms. They were carried on using the French national project Grid'5000 (part of the CoreGrid project) and the DG-ADAJ.
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byte code scheduling of Java programs with branches for desktop grid
Future Generation Computer Systems, 2007Co-Authors: Eryk Laskowski, Richard Olejnik, Marek Tudruj, Bernard TourselAbstract:A method for an introductory optimization of multithreaded Java programs for execution on clusters of Java Virtual Machines (JVMs) inside desktop grids is presented. It is composed of two stages. In the first stage, a clustering algorithm is applied to extended macro data flow graphs generated on the basis of the byte-code compiled for multithreaded Java programs. These graphs account for data and control dependencies in programs including conditional branch instructions annotated by branch statistics driven from execution traces for representative sets of data. In the second stage, a list scheduling is performed based on the Earliest Task First (ETF) heuristics in which node mapping on JVMs accounts for mutually exclusive paths outgoing from conditional branch nodes. The presented object placement optimization algorithm is a part of the DG-ADAJ environment.
Andreas Fuchs - One of the best experts on this subject based on the ideXlab platform.
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automatic scalable parallel test case execution introducing the munster distributed test case runner for Java midstr
ACM Symposium on Applied Computing, 2018Co-Authors: Vincent Von Hof, Andreas FuchsAbstract:Software testing is a broad research field and of great relevance to practitioners. Software testing involves multiple consecutive testing phases. One of these phases is the is the unit-testing phase, during which individual requirements of the software component are checked. Only once software-units and -components have been tested, can the testing process continue. Trivially, if this phase requires a great amount time, the testing process is delayed and in the worst case, the rollout of the system has to be rescheduled. Focusing on the widely adopted JUnit test framework, we propose a system to execute test cases on multiple distributed Virtual Machines. This paper presents an approach to connect multiple Java Virtual Machines (JVMs) into a test-net, which can execute a JUnit test suite in parallel. Furthermore, different distribution schemata are discussed. By distributing the execution to multiple VMs, the overall time to test is reduced, in order to improve the overall time required for the testing process.
Richard Olejnik - One of the best experts on this subject based on the ideXlab platform.
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Extremal Optimization Applied to Task Scheduling of Distributed Java Programs
Lecture Notes in Computer Science, 2013Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs
Lecture Notes in Computer Science, 2010Co-Authors: Ivanoe De Falco, Richard Olejnik, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Distributed Java Programs Initial Mapping Based on Extremal Optimization
Lecture Notes in Computer Science, 2010Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Data Mining on Desktop Grid Platforms
Lecture Notes in Computer Science, 2008Co-Authors: Valerie Fiolet, Richard Olejnik, Eryk Laskowski, Marek Tudruj, Łukasz Masko, Bernard TourselAbstract:Very large data volumes and high computation costs in data mining applications justify the use for them of Grid-level massive parallelism. The paper concerns Grid-oriented implementation of the DisDaMin (Distributed Data Mining) project, which proposes distributed knowledge discovery through parallelization of data mining tasks. DisDaMin solves data mining problems by using new distributed algorithms based on special clusterized data decomposition and asynchronous task processing, which match the Grid computing features. The DisDaMin algorithms are embedded inside the DG-ADAJ (Desktop-Grid Adaptative Application in Java) system, which is a middleware platform for Desktop Grid. It provides adaptive control of distributed applications written in Java for Grid or Desktop Grid. It allows an optimized distribution of applications on clusters of Java Virtual Machines, monitoring of application execution and dynamic on-line balancing of processing and communication. Simulations were performed to prove the efficiency of the proposed mechanisms. They were carried on using the French national project Grid'5000 (part of the CoreGrid project) and the DG-ADAJ.
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byte code scheduling of Java programs with branches for desktop grid
Future Generation Computer Systems, 2007Co-Authors: Eryk Laskowski, Richard Olejnik, Marek Tudruj, Bernard TourselAbstract:A method for an introductory optimization of multithreaded Java programs for execution on clusters of Java Virtual Machines (JVMs) inside desktop grids is presented. It is composed of two stages. In the first stage, a clustering algorithm is applied to extended macro data flow graphs generated on the basis of the byte-code compiled for multithreaded Java programs. These graphs account for data and control dependencies in programs including conditional branch instructions annotated by branch statistics driven from execution traces for representative sets of data. In the second stage, a list scheduling is performed based on the Earliest Task First (ETF) heuristics in which node mapping on JVMs accounts for mutually exclusive paths outgoing from conditional branch nodes. The presented object placement optimization algorithm is a part of the DG-ADAJ environment.
Eryk Laskowski - One of the best experts on this subject based on the ideXlab platform.
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Extremal Optimization Applied to Task Scheduling of Distributed Java Programs
Lecture Notes in Computer Science, 2013Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Extremal Optimization Approach Applied to Initial Mapping of Distributed Java Programs
Lecture Notes in Computer Science, 2010Co-Authors: Ivanoe De Falco, Richard Olejnik, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Distributed Java Programs Initial Mapping Based on Extremal Optimization
Lecture Notes in Computer Science, 2010Co-Authors: Richard Olejnik, Ivanoe De Falco, Eryk Laskowski, Umberto Scafuri, Ernesto Tarantino, Marek TudrujAbstract: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.
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Data Mining on Desktop Grid Platforms
Lecture Notes in Computer Science, 2008Co-Authors: Valerie Fiolet, Richard Olejnik, Eryk Laskowski, Marek Tudruj, Łukasz Masko, Bernard TourselAbstract:Very large data volumes and high computation costs in data mining applications justify the use for them of Grid-level massive parallelism. The paper concerns Grid-oriented implementation of the DisDaMin (Distributed Data Mining) project, which proposes distributed knowledge discovery through parallelization of data mining tasks. DisDaMin solves data mining problems by using new distributed algorithms based on special clusterized data decomposition and asynchronous task processing, which match the Grid computing features. The DisDaMin algorithms are embedded inside the DG-ADAJ (Desktop-Grid Adaptative Application in Java) system, which is a middleware platform for Desktop Grid. It provides adaptive control of distributed applications written in Java for Grid or Desktop Grid. It allows an optimized distribution of applications on clusters of Java Virtual Machines, monitoring of application execution and dynamic on-line balancing of processing and communication. Simulations were performed to prove the efficiency of the proposed mechanisms. They were carried on using the French national project Grid'5000 (part of the CoreGrid project) and the DG-ADAJ.
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byte code scheduling of Java programs with branches for desktop grid
Future Generation Computer Systems, 2007Co-Authors: Eryk Laskowski, Richard Olejnik, Marek Tudruj, Bernard TourselAbstract:A method for an introductory optimization of multithreaded Java programs for execution on clusters of Java Virtual Machines (JVMs) inside desktop grids is presented. It is composed of two stages. In the first stage, a clustering algorithm is applied to extended macro data flow graphs generated on the basis of the byte-code compiled for multithreaded Java programs. These graphs account for data and control dependencies in programs including conditional branch instructions annotated by branch statistics driven from execution traces for representative sets of data. In the second stage, a list scheduling is performed based on the Earliest Task First (ETF) heuristics in which node mapping on JVMs accounts for mutually exclusive paths outgoing from conditional branch nodes. The presented object placement optimization algorithm is a part of the DG-ADAJ environment.
Bertil Folliot - One of the best experts on this subject based on the ideXlab platform.
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I-JVM: a Java Virtual Machine for Component Isolation in OSGi
2009Co-Authors: Nicolas Geoffray, Gaël Thomas, Gilles Muller, Pierre Parrend, Stéphane Frénot, Bertil FolliotAbstract:The OSGi framework is a Java-based, centralized, component oriented platform. It is being widely adopted as an execution environment for the development of extensible applications. However, current Java Virtual Machines are unable to isolate components from each other. For instance, a malicious component can freeze the complete platform by allocating too much memory or alter the behavior of other components by modifying shared variables. This paper presents I-JVM, a Java Virtual Machine that provides a lightweight approach to isolation while preserving compatibility with legacy OSGi applications. Our evaluation of I-JVM shows that it solves the 8 known OSGi vulnerabilities that are due to the Java Virtual Machine and that the overhead of I-JVM compared to the JVM on which it is based is below 20%.
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I-JVM: a Java Virtual Machine for Component Isolation in OSGi
2009Co-Authors: Nicolas Geoffray, Gaël Thomas, Gilles Muller, Pierre Parrend, Stéphane Frénot, Bertil FolliotAbstract:The OSGi framework is a Java-based, centralized, component oriented platform. It is being widely adopted as an execution environment for the development of extensible applications. However, current Java Virtual Machines are unable to isolate components from each other. For instance, a malicious component can freeze the complete platform by allocating too much memory or alter the behavior of other components by modifying shared variables. This paper presents I-JVM, a Java Virtual Machine that provides a lightweight approach to isolation while preserving compatibility with legacy OSGi applications. Our evaluation of I-JVM shows that it solves the 8 known OSGi vulnerabilities that are due to the Java Virtual Machine. Overall, the overhead of I-JVM compared to the JVM on which it is based is below 20%.
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DSN - I-JVM: a Java Virtual Machine for component isolation in OSGi
2009 IEEE IFIP International Conference on Dependable Systems & Networks, 2009Co-Authors: Nicolas Geoffray, Gaël Thomas, Gilles Muller, Pierre Parrend, Stéphane Frénot, Bertil FolliotAbstract:The OSGi framework is a Java-based, centralized, component oriented platform. It is being widely adopted as an execution environment for the development of extensible applications. However, current Java Virtual Machines are unable to isolate components from each other. For instance, a malicious component can freeze the complete platform by allocating too much memory or alter the behavior of other components by modifying shared variables. This paper presents I-JVM, a Java Virtual Machine that provides a lightweight approach to isolation while preserving compatibility with legacy OSGi applications. Our evaluation of I-JVM shows that it solves the 8 known OSGi vulnerabilities that are due to the Java Virtual Machine and that the overhead of I-JVM compared to the JVM on which it is based is below 20%.
