Fuzzy clustering based scheduling algorithm for minimizing the tasks completion time in cloud computing environment.

This paper explores the complexity of project planning in a cloud computing environment and recognizes the challenges associated with distributed resources, heterogeneity, and dynamic changes in workloads. This research introduces a fresh approach to planning cloud resources more effectively by utilizing fuzzy waterfall techniques. The goal is to make better use of resources while cutting down on scheduling costs. By categorizing resources based on their characteristics, this method aims to lower search costs during project planning and speed up the resource selection process. The paper presents the Budget and Time Constrained Heterogeneous Early Completion (BDHEFT) technique, which is an enhanced version of HEFT tailored to meet specific user requirements, such as budget constraints and execution timelines. With its focus on fuzzy resource allocation that considers task composition and priority, BDHEFT streamlines the project schedule, ultimately reducing both execution time and costs. The algorithm design and mathematical modeling discussed in this study lay a strong foundation for boosting task scheduling efficiency in cloud computing environments, which provides a broad perspective to improve the overall system performance and meet user quality requirements.