Energy-Efficient Task Scheduling in Cloud Data Centers: A Green Computing Perspective

As digital ecosystems continue to grow, cloud data centers have emerged as critical infrastructure. However, their rising energy demands present serious challenges in terms of cost and environmental sustainability, but they consume substantial amounts of energy, contributing to both operational costs and environmental impact. Green computing strategies aim to optimize energy usage without sacrificing performance or service quality. This paper presents a comprehensive study on energy-efficient task scheduling mechanisms in cloud data centers from a green computing standpoint. We analyze various scheduling models, discuss their trade-offs, and propose a conceptual framework that leverages dynamic workload management, virtualization-aware scheduling, and machine learning-based predictions to reduce energy consumption. The framework seeks to optimize energy use while maintaining acceptable performance thresholds, supporting sustainability in large-scale cloud environments.