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Multi-objective and multi constrained task scheduling framework for computational grids.

Sujay N Hegde1, D B Srinivas2, M A Rajan3

  • 1University of California USA, Irvine, CA, USA.

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Summary
This summary is machine-generated.

This study introduces an efficient multi-objective task scheduling framework for heterogeneous grid networks. The proposed system optimizes task scheduling to minimize costs and turnaround time while maximizing grid utilization.

Keywords:
Direct acyclic graphGrid computingGridSimScientific graphTOPSIS

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Computational Science

Background:

  • Grid computing facilitates large-scale parallel applications.
  • Task scheduling on computational grids is an NP-complete problem.
  • Existing algorithms often focus on single objectives like makespan or cost.

Purpose of the Study:

  • To propose an efficient multi-objective task scheduling framework for heterogeneous grid networks.
  • To address user needs for lower cost and latency, and provider needs for high utilization and profitability.
  • To balance trade-offs among multiple scheduling objectives.

Main Methods:

  • Developed a multi-objective optimization framework for task scheduling.
  • Designed a scheduler to minimize turnaround time, communication, and execution costs.
  • Utilized the GridSim simulator for performance evaluation.

Main Results:

  • The proposed framework effectively minimizes key performance metrics.
  • Demonstrated improved grid utilization through experimental validation.
  • Successfully balanced multiple competing objectives in task scheduling.

Conclusions:

  • The developed framework offers an efficient solution for scheduling computationally intensive tasks on grids.
  • Multi-objective optimization is crucial for balancing user and provider requirements in grid environments.
  • The approach shows promise for enhancing the performance of heterogeneous grid networks.