Priority-Driven Task Scheduling and Resource Allocation in Cloud Environment

Currently, efficient task scheduling and resource allocation processes are crucial for optimizing performance in cloud computing environments. In this paper, we introduce a novel approach that prioritizes tasks based on their specific requirements and allocates resources accordingly. Task priority is determined through a scoring threshold that considers different requirements such as deadline, temporal requirements, and task length. To ensure efficient matching, virtual machines (VMs) are categorized into three distinct groups using K-means clustering. Simulations conducted within the CloudSim simulator demonstrate the effectiveness of this method, leading to significant reductions in makespan and energy consumption, while simultaneously improving resource utilization compared to existing algorithms.