Improved Sine-Cosine Nomadic People Optimizer (NPO) for Large and Synthetic Extra-large Scientific Workflow Task Scheduling Optimization in Cloud Environment
DOI:
https://doi.org/10.56294/dm20251000Keywords:
Workflow task scheduling, Heterogeneous cloud, Synthetic extra-large workflows, Nomadic People Optimizer, Multi-swarm optimization, Makespan, Sine-Cosine optimizationAbstract
Cloud computing has become an increasingly fundamental technology in recent years, influencing many different areas of the economy. It offers significant features such as greater scalability, on-demand resource allocation for varied workflows, and a pay-as-you-go pricing system. For cloud service providers, efficient and optimized scheduling is essential since it lowers resources consumption, operation expenses, and guarantees users' service level agreements. However, scheduling optimization becomes increasingly challenging due to the inherent heterogeneity of cloud resources and the growing scale of workflows. To tackle these issues, this study presents hybrid Sine-Cosine Nomadic People Optimizer (called QNPO) aimed at optimization of multi-objective cloud task scheduling with a special emphasis on large and extra-large scientific workflow. Sixteen synthetic extra-large heterogeneous workflows datasets were composed in this study and used to evaluate the proposed approach on a heterogeneous cloud infrastructure configure in Workflow Sim. The results indicated that the QNPO consistently outperformed traditional optimization algorithms in all proposed evaluation scenarios, achieving a significant improvement in scheduling efficiency between 30 and 60 %.
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Copyright (c) 2025 Saif Hameed, Hend Marouane, Ahmed Fakhfakh, Sinan Salih (Author)
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