Energy-Aware Task Scheduling Models for Green Cloud Computing

Abstract

The rapid growth of cloud computing services has led to an exponential increase in energy consumption by data centers, raising concerns about environmental sustainability and operational costs. Energy-aware task scheduling has emerged as a critical strategy to address these challenges by optimizing the allocation and execution of tasks to minimize power usage while maintaining service quality. This paper presents a comprehensive study of energy-aware task scheduling models tailored for green cloud computing environments. We explore hybrid and heuristic scheduling approaches that balance energy efficiency with performance metrics such as task completion time and resource utilization. Our study involves the design and evaluation of novel scheduling algorithms that leverage dynamic voltage and frequency scaling (DVFS), task consolidation, and workload prediction to reduce energy consumption in cloud data centers. Simulation results demonstrate significant improvements in energy savings without compromising system throughput or user quality of service (QoS). The models also account for heterogeneous cloud resources, enabling effective task distribution in complex cloud infrastructures.

The paper further discusses the trade-offs between energy efficiency and computational overhead, providing guidelines for deploying energy-aware schedulers in real-world cloud environments. By integrating energy metrics into scheduling decisions, these models contribute to greener computing practices, reducing carbon footprints while ensuring reliable service delivery. Our findings highlight the importance of adaptive and predictive scheduling mechanisms in achieving sustainable cloud operations.

Future research directions include the incorporation of renewable energy sources, advanced machine learning techniques for workload prediction, and real-time adaptive scheduling to enhance the scalability and robustness of energy-aware task scheduling frameworks.