An improved genetic algorithm for efficient scheduling on distributed memory parallel systems

A key issue related to the distributed memory multiprocessors architecture for achieving high performance computing is the efficient scheduling of heavily communicated parallel applications such that the total execution time is minimized. Therefore, this paper provides a genetic algorithm based on task clustering techniques for scheduling parallel applications with large communication delays on distributed memory parallel systems. The genetic algorithm is improved with the introduction of some extra knowledge about the scheduling problem. This knowledge is represented by a class of clustering heuristic which is based on structural properties of the parallel application. The major feature of the proposed algorithm is that it takes advantage of the effectiveness of task clustering for reducing communication delays combined with the ability of the genetic algorithms for exploring and exploiting information of the search space of the scheduling problem. The algorithm is assessed by simulation run on some families of traced graphs which represents some of the numerical parallel application programs, and a set of randomly generated applications. Simulation results showed that this algorithm significantly improves the performance of related approaches

Memorias del Congreso Latinoamericano de Computación de Alto Rendimiento (CLCAR) - Sobre la calendarización en la Grid

Sobre la calendarización en la Grid. (Pecero Sánchez, Johnatan E.) Resumen En este artículo revisamos el problema de la calendarización en la Grid. Se hace un repaso principalmente desde el punto de vista de los algoritmos de calendarización. Algunos retos para los tradicionales algoritmos de calendarización frente al problema de calendarización en la Grid son identificados. Se discuten brevemente algunas diferencias entre la calendarización tradicional y la calendarización en la Grid. Entonces, describimos algunas metodologías tradicionales que podrían ser investigadas en el problema de calendarización en la Grid. Abstract In this paper, we review the Grid scheduling problem of point of view of traditional scheduling algorithms. In this survey, some challenges for traditional scheduling algorithms when scheduling in Grid computing are identified. We first survey the traditional scheduling problem. After, we focus on Grid computing and Grid scheduling. We discuss briefly difference between traditional scheduling and Grid scheduling. Then, we describe some approaches already adopted in traditional scheduling which could be investigated in Grid scheduling. Ponencia publicada en: Memorias del Congreso Latinoamericano de Computación de Alto Rendimiento (CLCAR) Santa Marta, Colombia 13 al 18 de agosto 2007. J.C. Jaime y G. Díaz (editores), Publicaciones Univ. Industrial de Santander, Bucaramanga, Colombia (2007)[email protected] analític

Scheduling with uncertainties on new computing platforms

New distributed computing platforms (grids) are based on interconnections of a large number of processing elements. A most important issue for their effective utilization is the optimal use of resources through proper task scheduling. It consists of allocating the tasks of a parallel program to processors on the platform and to determine at what time the tasks will start their execution. As data may be subject to uncertainties or disturbances, it is practically impossible to precisely predict the input parameters of the task scheduling problem. We briefly survey existing approaches for dealing with data uncertainties and discuss their relevance in the context of grid computing. We describe the stabilization process and analyze a scheduling algorithm that is intrinsically stable (i.e., it mitigates the effects of disturbances in input data at runtime). This algorithm is based on a decomposition of the application graph into convex sets of vertices. Finally, it is compared experimentally to pure on-line and well-known off-line algorithms

Scalable, Low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems

Forheterogeneousdistributedcomputingsystems,importantdesignissues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report and evalu- ate three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. We conduct a comprehensive performance evaluation study us- ing simulation. The benchmarking outlines the performance of the schedulers, rep- resenting scalability, makespan, flowtime, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as good as the traditional approaches, for makespan and flowtime, while featuring lower com- plexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail

Etude de l'intéraction dans les ordonnanceurs parallèles

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