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Genetic-Based Two Granularity Ordering Methods for Multiple Workflow Scheduling
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DOI：10.1109/ACCESS.2023.3337832.png)
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Abstract
En 中文
In cloud computing, multiple workflow scheduling is important to optimize resource allocation and utilization for concurrent execution of diverse workflows across different applications. While previous research has focused on clustering-based resource allocation to reduce communication overheads by grouping tasks, it often overlooks the significance of task execution ordering, limiting overall performance optimization. To address this limitation, we propose two genetic-based approaches, considering task and cluster-level characteristics, to introduce novel ordering techniques for multi-workflow scheduling under cluster-based resource allocation. By comparing two granularity ordering methods, we offer valuable insights for efficient task management in multi-workflow environments. Our experiments demonstrate that the proposed approaches, especially the task granularity-based ordering method, outperform existing primary clustering methods, particularly for scenarios involving a large number of workflows or highly parallel workflows.
Keywords:
Task analysis
Costs
Scheduling
Resource management
Optimization
Clustering algorithms
Quality of service
Cloud computing
multi-workflow
multi-objective optimization
task ordering
cluster ordering
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W