Scientific workflows are crucial for handling extensive datasets and facilitating largescale scientific research. They are time-consuming and resource-intensive applications, rendering dispersed technologies like cloud computing particularly appropriate for their implementation. Nonetheless, cloud systems provide distinct issues, especially in task scheduling and data location, which must be resolved for optimal workflow execution. Despite much research on workflow scheduling, the integrated optimization of task scheduling and data placement requires additional literature research. This research introduces an innovative scheduling framework that concurrently tackles task scheduling and data placement for scientific workflows in cloud environments. The proposed work incorporates a genetic algorithm for optimizing scheduling and data placement, combined with a fuzzy data placement strategy utilizing the Interval Type-2 Fuzzy C-Means (IT2FCM) clustering method, which adeptly addresses data uncertainty in cloud storage. The suggested scheduler significantly minimizes data transmission time and enhances overall workflow performance by dynamically coordinating data allocation with task execution. This study’s optimization methodology integrates evolutionary computation with fuzzy clustering. It offers a more comprehensive and adaptable method for managing scientific workflows in the cloud. The proposed scheduler was executed using a simulated cloud environment on real-world workflows, such as Epigenomics and LIGO from Pegasus. The experimental results show that the proposed approach reduces data movements significantly compared to the literature.
Kchaou et al. (Thu,) studied this question.
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