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Cloud computing

Enhanced Workflow Scheduling in Cloud Computing

Strategic Insights

— 1 min read

Researchers at Dr. Harisingh Gour Central University have made significant progress in optimizing workflow execution in cloud computing environments. The team has proposed a Modified Fork and Join Scheduling algorithm, termed as MFJS, which aims to minimize makespan, reduce resource utilization, and enhance overall efficiency. The study utilized real scientific workflows and compared the performance of MFJS with existing algorithms, demonstrating substantial improvements in makespan reduction and Quality of Service (QoS) metrics.

Key Takeaways:

  • A Modified Fork and Join Scheduling algorithm, MFJS, has been proposed to optimize workflow execution in cloud computing environments.
  • The algorithm focuses on minimizing makespan, reducing resource utilization, and enhancing overall efficiency.
  • MFJS has been compared with state-of-the-art algorithms, including HEFT, CPOP, ALAP, QLHEFT, and Modified Min-Min, demonstrating substantial improvements in makespan reduction.
  • In the examined example, MFJS reduced makespan by approximately 13% for HEFT, 3% for ALAP, and 29% for CPOP.
  • The proposed algorithm aims to achieve optimal makespan and efficient resource utilization by considering the workflow's dependency on resource constraints.
  • The study has been peer-reviewed, with ANOVA testing used to validate the results.
  • Various Quality of Service (QoS) metrics, including Speedup, Efficiency, and Average Resource Utilization, have been used for performance analysis.

Statistics:

  • 13% reduction in makespan for HEFT algorithm
  • 3% reduction in makespan for ALAP algorithm
  • 29% reduction in makespan for CPOP algorithm
  • 28% (13% + 3% + 12%) overall makespan reduction for MFJS algorithm compared to HEFT, ALAP, and CPOP

Sources:

  • "Enhancing Fork and Join Based Workflow Scheduling Model In Cloud Computing" (Cluster Computing, 2025;28(13))
  • Springer (www.springer.com)
  • Cluster Computing (www.springerlink.com/content/1386-7857/)
  • NewsRx LLC (Copyright 2025)
  • "Researchers at Dr. Harisingh Gour Central University Release New Data on Cloud Computing (Enhancing Fork and Join Based Workflow Scheduling Model In Cloud Computing)" (Information Technology Newsweekly, October 21, 2025; p 735)

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