Analyzing Dynamic Workload Management and Energy-Efficient Task Scheduling in Cloud Computing
M. Prashanthi, B. Lalitha · 2024
Cloud computing provides flexible and scalable solutions for diverse applications, but effective task scheduling remains a critical challenge that significantly impacts performance, resource allocation, and operational costs. This paper examines the complexities of task scheduling in dynamic cloud environments, with a particular emphasis on optimizing energy efficiency and improving workload management. Traditional heuristic-based approaches often struggle to address the challenges posed by dynamic, large-scale cloud systems. The paper reviews energy-efficient scheduling strategies for their potential to reduce energy consumption while maintaining system performance. Furthermore, it explores advanced scheduling techniques, including hybrid heuristics, machine learning-based methods, and optimization algorithms. By critically analyzing the current state of research and identifying key challenges, this study aims to propose innovative solutions to enhance resource utilization, minimize energy costs, and improve the overall efficiency of cloud task scheduling.