Conference Paper

A Fault Tolerant Adaptive Method for the Scheduling of Tasks in Dynamic Grids

Grupo de Química Computacional y Computación de Alto Rendimiento; Escuela Superior de Informática, Universidad de Castilla-La Mancha, 13071; Ciudad Real, Spain
DOI: 10.1109/ADVCOMP.2009.15 Conference: Advanced Engineering Computing and Applications in Sciences, 2009. ADVCOMP '09. Third International Conference on

ABSTRACT An essential issue in distributed high-performance computing is how to allocate efficiently the workload among the processors. This is specially important in a computational Grid where its resources are heterogeneous and dynamic. Algorithms like Quadratic Self-Scheduling (QSS) and Exponential Self-Scheduling (ESS) are useful to obtain a good load balance, reducing the communication overhead. Here, it is proposed a fault tolerant adaptive approach to schedule tasks in dynamic Grid environments. The aim of this approach is to optimize the list of chunks that QSS and ESS generates, that is, the way to schedule the tasks. For that, when the environment changes, new optimal QSS and ESS parameters are obtained to schedule the remaining tasks in an optimal way, maintaining a good load balance. Moreover, failed tasks are rescheduled. The results show that the adaptive approach obtains a good performance of both QSS and ESS even in a highly dynamic environment.

0 Bookmarks
 · 
13 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Dynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: on-line and batch mode heuristics. Three new heuristics, one for batch and two for on-line, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five on-line heuristics and three batch heuristics are examined. The on-line heuristics consider; to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of mapping heuristic depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, (b) the optimization requirements, and (c) the arrival rate of the tasks
    Heterogeneous Computing Workshop, 1999. (HCW '99) Proceedings. Eighth; 02/1999
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
    Addison-Wesley, Reading, Massachusetts. 01/1989;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Ensembles of distributed, heterogeneous resources, also known as computational grids, have emerged as critical platforms for high-performance and resource-intensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in heterogeneous, multiuser grid environments. We discuss the AppLeS project and outline our findings.
    IEEE Transactions on Parallel and Distributed Systems 05/2003; · 1.80 Impact Factor

Full-text (2 Sources)

View
9 Downloads
Available from
May 17, 2014