Adaptive Scheduling for Task Farming with Grid Middleware

  • Authors:

  • Henri Casanova;Myungho Kim;James S. Plank;Jack J. Dongarra

  • Affiliations:

  • Department of Computer Science and Engineering, University of California at San Diego, La Jolla, California, U.S.A.;School of Computing, Soongsil University, Seoul, Korea;Department of Computer Science, University of Tennessee, Knoxville, Tennessee, U.S.A.;Department of Computer Science, University of Tennessee, Knoxville, and Mathematical Science Section, Oak Ridge National Laboratory, Tennessee, U.S.A.

  • Venue:
  • International Journal of High Performance Computing Applications
  • Year:
  • 1999

Quantified Score

Hi-index 0.00

Visualization

Abstract

Scheduling in metacomputing environments is an active field of research as the vision of a Computational Grid becomes more concrete. An important class of Grid applications are long-running parallel computations with large numbers of somewhat independent tasks (Monte Carlo simulations, parameter-space searches, etc.). A number of Grid middleware projects are available to implement such applications, but scheduling strategies are still open research issues. This is mainly due to the diversity of both Grid resource types and their availability patterns. The purpose of this work is to develop and validate a general adaptive scheduling algorithm for task farming applications along with a user interface that makes the algorithm accessible to domain scientists. The authors' algorithm is general in that it is not tailored to a particular Grid middleware and it requires very few assumptions concerning the nature of the resources. Their first testbed is NetSolve as it allows quick and easy development of the algorithm by isolating the developer from issues such as process control, I/O, remote software access, or fault-tolerance.