Authors: Arjun Prasad
Abstract: Artificial intelligence (AI) has emerged as a transformative force across numerous technological domains, with its impact acutely felt in the design and operation of modern data centers. As the demand for cloud services, big data analytics, and internet-based applications surges, data centers have grown exponentially in size and complexity, concurrently escalating their energy consumption. Addressing energy efficiency within these large-scale computing infrastructures is paramount not only from an operational cost perspective but also for environmental sustainability. AI-based workload schedulers have been increasingly adopted as innovative solutions to optimize resource utilization and curtail energy wastage. These intelligent schedulers leverage machine learning algorithms, predictive analytics, and real-time monitoring to dynamically allocate workloads based on energy profiles, cooling capacities, and computing requirements. The integration of AI fosters adaptive scheduling strategies that can respond to fluctuating workloads, minimize idle hardware, and optimize server usage, thereby enhancing energy efficiency. This article comprehensively explores the multifaceted impact of AI-driven workload scheduling on the operation of energy-efficient data centers. It delves into state-of-the-art AI scheduling techniques, mechanisms for workload prediction, energy consumption modeling, and the synergies between hardware infrastructure and intelligent scheduling systems. Furthermore, the article discusses challenges such as scalability, algorithmic complexity, and integration with existing data center management frameworks. By synthesizing contemporary research findings and industry practices, this work aims to provide a detailed understanding of how AI can revolutionize energy management in data centers, ultimately contributing to reduced carbon footprints and sustainable growth in the digital era.
DOI: http://doi.org/10.5281/zenodo.17777795
