Scholarlink

<p>Cloud computing provides a scalable and flexible platform for executing scientific workflows. Workflow ensembles, which consist of workflows with similar structures but varying input data and sizes, require efficient scheduling to optimize resource utilization, reduce costs, and improve workflow completion rates. Traditional scheduling methods often leave idle time slots between tasks, leading to resource wastage and decreased system efficiency. This paper presents Workflow Ensemble Scheduling with Gap analysis (WESG), a novel approach designed to overcome these challenges while adhering to budget and deadline constraints. WESG employs a five-step algorithm to analyze and reduce resource idleness, optimizing task allocation and resource provisioning. By effectively utilizing these gaps, WESG enhances resource efficiency, prioritizes high-priority workflows, and increases the overall number of completed workflows. Experimental results show that WESG outperforms traditional scheduling techniques, achieving better resource utilization, reducing idle gaps, and significantly improving workflow completion rates. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Workflow ensemble scheduling in IaaS cloud: a gap analysis perspective under deadline and budget constraints

  • 期刊论文

  • 发表日期：

    2025年7月17日

查看全文
  • Negin Shafinezhad,
  • Hamid Abrishami,
  • Behrooz Zolfaghari,
  • Saeid Abrishami,
  • Anahita Morvaridi

摘要

Cloud computing provides a scalable and flexible platform for executing scientific workflows. Workflow ensembles, which consist of workflows with similar structures but varying input data and sizes, require efficient scheduling to optimize resource utilization, reduce costs, and improve workflow completion rates. Traditional scheduling methods often leave idle time slots between tasks, leading to resource wastage and decreased system efficiency. This paper presents Workflow Ensemble Scheduling with Gap analysis (WESG), a novel approach designed to overcome these challenges while adhering to budget and deadline constraints. WESG employs a five-step algorithm to analyze and reduce resource idleness, optimizing task allocation and resource provisioning. By effectively utilizing these gaps, WESG enhances resource efficiency, prioritizes high-priority workflows, and increases the overall number of completed workflows. Experimental results show that WESG outperforms traditional scheduling techniques, achieving better resource utilization, reducing idle gaps, and significantly improving workflow completion rates.