Energy-Efficient Computing Models for Sustainable Data Centers

Abstract

The accelerating global demand for data center services has intensified the need for energy-efficient computing models, essential for sustainable data center operations. This paper examines cutting-edge 2023 developments in optimizing both compute and cooling energy, leveraging dynamic resource management, hybrid evolutionary algorithms, automation-driven resource scaling, and novel architectural paradigms. We first review metaheuristic-based strategies that jointly optimize computing and thermal energy consumption, achieving up to 21.7% energy efficiency gains while preserving service quality arXiv. Next, we explore SHIELD, a hybrid evolutionary learning framework that co-optimizes carbon emissions, water usage, and energy costs for geo-distributed data centers, yielding up to 3.7× reduction in carbon footprint and 1.8× lower water use arXiv. We also present the Full Scaling Automation (FSA) approach for dynamic CPU utilization control using deep learning, which realized significant electricity savings (1.54 million kWh) and CO₂ reduction (947 tons) during an industrial deployment in China arXiv. Finally, we discuss innovative design models like high-altitude platform–enabled data centers that can cut energy usage by 14% via natural cooling and solar energy harvesting arXiv. Through comparative analysis, we highlight how these models integrate real-time demand prediction, intelligent scheduling, and environmental awareness to significantly advance data center sustainability. We conclude with recommendations for hybrid frameworks combining dynamic resource optimization with architectural innovations to meet escalating compute demands while achieving climatealigned energy efficiency.