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Sort-Mid tasks scheduling algorithm in grid computing.

Naglaa M Reda1, A Tawfik2, Mohamed A Marzok2

  • 1Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt.

Journal of Advanced Research
|December 9, 2015
PubMed
Summary

A new Sort-Mid heuristic algorithm enhances grid computing by maximizing resource utilization and minimizing task completion time. This approach optimizes scheduling for heterogeneous systems, outperforming existing methods.

Keywords:
Grid computingHeuristic algorithmMakespanResource utilizationScheduling

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Area of Science:

  • Grid Computing
  • Algorithm Optimization
  • Distributed Systems

Background:

  • Task scheduling on heterogeneous grid resources is an NP-complete problem.
  • Existing algorithms show good performance in resource selection but struggle with full resource utilization.
  • Maximizing resource utilization and minimizing makespan remain key challenges in grid computing.

Purpose of the Study:

  • To propose a novel heuristic algorithm, Sort-Mid, for optimizing task scheduling in grid computing.
  • To enhance resource utilization and minimize the makespan (total completion time) of tasks.
  • To address the challenge of fully leveraging heterogeneous resources in distributed systems.

Main Methods:

  • The Sort-Mid algorithm calculates the average completion time for each task.
  • It identifies the task with the maximum average completion time.
  • This task is then assigned to the resource with the minimum completion time, and the process iterates.

Main Results:

  • Experimental tests demonstrate that Sort-Mid significantly improves resource utilization.
  • The algorithm effectively minimizes the makespan compared to other scheduling algorithms.
  • Sort-Mid shows superior performance in balancing task allocation across heterogeneous grid resources.

Conclusions:

  • The Sort-Mid algorithm presents an effective heuristic for task scheduling in grid computing environments.
  • It successfully addresses the limitations of previous methods by enhancing resource utilization and reducing makespan.
  • This approach offers a promising strategy for optimizing distributed computing performance.