Details

Autor(en) / Beteiligte

• Li, Feng
• Tan, Wen Jun
• Seok, Moon Gi
• Cai, Wentong

Titel

Genetic-Based Two Granularity Ordering Methods for Multiple Workflow Scheduling

Ist Teil von

• IEEE access, 2024, Vol.12, p.1747-1760

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2024

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.