Dynamic Load Balancing Based on Applications Global States Monitoring

8 pages à paraîtreInternational audienceThe paper presents how to use a special novel distributed program design framework with evolved global control mechanisms to assure processor load balancing during execution of application programs. The new framework supports a programmer with an API and GUI for automated graphical design of program execution control based on global application states monitoring. The framework provides highlevel distributed control primitives at process level and a special control infrastructure for global asynchronous execution control at thread level. Both kinds of control assume observations of current multicore processor performance and communication throughput enabled in the executive distributed system. Methods for designing processor load balancing control based on a system of program and system properties metrics and computational data migration between application executive processes is presented and assessed by experiments with execution of graph representations of distributed programs

Embedded Parallel Systems Based on Dynamic Look-Ahead Reconfiguration in Redundant Communication Resources

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A Framework for Desktop GRID Applications: CCADAJ

International audienceWe propose a new component framework over a middleware platform for GRID computing: DG-ADAJ (Desktop GRID - Adaptive Distributed Application in Java). Our platform allows to have a Single System Image (SSI) of the GRID platform. It gives a specialmechanism at middleware level which assures dynamic and automatic adaptation to variations of computation methods and execution platform. DG-ADAJ gives also specific mechanisms based on control components which helps a users to build component-based parallel/distributed applications. We use the CCA (Common Component Architecture) as a component architecture model for our framework. CCADAJ (CCA-ADAJ) is a layer above the DG-ADAJ environment, which helps user to build his parallel/distributed applications by assembling components in a transparent way and which exploits the parallelism of the environment

Load Balancing Metrics for the SOAJA Framework

International audienceThe paper describes system and program metrics used for load balancing algorithms for Java program execution in the SOAJA (Service Oriented Adaptative Java Applications) executive environment. This environment aims in maintaining design and execution of large scale computing tasks in complex networked Grid environments. SOAJA services provide means for static and dynamic load balancing with the use of special metrics obtained by Java object observation. SOAJA comprises mechanisms and algorithms for automatic placement and adaptation of application objects, in response to evolution of resource availability. Under control of SOAJA, parallel Java objects can be optimally allocated to Grid nodes before execution and next migrated at runtime to less loaded nodes to maintain the balance of loads of constituent JVMs. SOAJA mechanisms employ computation power metrics based on measurements of the idle time of processor nodes and communication bandwidth metrics for network resources based on statistical assessment of the existing traffic. Due to these mechanisms the granularity of computing and distribution of the application elements on the Grid platform can be optimally controlled

A DISTRIBUTED PROGRAM GLOBAL EXECUTION CONTROL ENVIRONMENT APPLIED TO LOAD BALANCING

Abstract. The paper is concerned with a new distributed program design environment based on the global application states monitoring. The environment called PEGASUS (from Program Execution Governed by Asynchronous SUpervision of States) supplies to a programmer a ready to use control primitives to design distributed program execution control in which decisions for synchronous and asynchronous control actions are based on predicates evaluated on global application states. Such strongly consistent global application states are automatically constructed by the run-time system which additionally provides mechanisms for their analysis and organizing the respective program execution control in processes and threads of user programs executed in multicore processors. The PEGASUS control mechanisms are graphically supported in the respective program design framework. The paper first presents main general features of the PEGASUS environment. Next, it presents a method for load balancing inside distributed programs based on a set of parameters which are dynamically measured during program execution. Then, the paper presents how the described load balancing method can be implemented inside the PEGASUS environment taking as an example distributed programs for solving the Traveling Salesman Problem (TSP). Key words: distributed program design tools, global application states monitoring, load balancing; 1. Introduction. Distribute

Data Mining on Desktop Grid Platforms

International audienceVery 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

Optimizing distributed data mining applications based on object clustering methods

International audienceThe exponential computational cost involved in traditional data mining methods enforces search for less complex new algorithms. Especially, data mining on Grid is a challenge due to the lack of shared memory in Grid computing, which puts special attention to communication optimization. The aim of the DisDaMin project (Distributed Data Mining), descibed in the paper, is solving data mining problems by using new distributed algorithms intented for execution in Grid environments. The DisDaMin implements intelligent fragmentation of data by clustering methods and asynchronous collaborative processing adjusted to Grid environments. The DG-ADAJ environment provides adaptive control of distributed applications written in Java for Desktop Grid. It constitutes a component-based middleware, which allows for optimized distribution of applications on clusters of Java Virtual Machines, monitoring of application execution and dynamic on-line balancing of processing and communication. The DG-ADAJ system provides a middleware platform for Desktop Grid that could be used as a deployment base for DisDaMin algorithms. In this paper, we propose static object placement optimization algorithms for fragmentation of data in the DisDaMin project. The algorithms use DG-ADAJ's object clustering methods to provide optimized local processing on each node with minimized inter-node communication

Parallel extremal optimization in processor load balancing for distributed applications

International audienceThe paper concerns parallel methods for extremal optimization (EO) applied in processor load balancingin execution of distributed programs. In these methods EO algorithms detect an optimized strategy oftasks migration leading to reduction of program execution time. We use an improved EO algorithmwith guided state changes (EO-GS) that provides parallel search for next solution state during solutionimprovement based on some knowledge of the problem. The search is based on two-step stochasticselection using two fitness functions which account for computation and communication assessment ofmigration targets. Based on the improved EO-GS approach we propose and evaluate several versions ofthe parallelization methods of EO algorithms in the context of processor load balancing. Some of them usethe crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluatedby experiments with simulated load balancing in execution of distributed programs represented as macrodata flow graphs. Load balancing based on so parallelized improved EO provides better convergence ofthe algorithm, smaller number of task migrations to be done and reduced execution time of applications