Energy-Aware Algorithms and System-Level Optimization Techniques for Sustainable Computing in Large-Scale Data Centers
Keywords:
Energy-aware computing, sustainable data centers, system-level optimization, workload scheduling, green computing, power efficiency, dynamic resource managementAbstract
As global computing demand continues its exponential growth, the energy consumption of large-scale data centers has emerged as a critical sustainability concern. This paper explores energy-aware algorithms and system-level optimization techniques designed to reduce the environmental and operational footprint of data centers. Building upon advances, we review contemporary literature and present a cohesive view of energy optimization across hardware, software, and workload management levels. A hybrid approach combining dynamic resource allocation, machine learning-based prediction, and renewable energy integration is shown to improve energy efficiency by up to 40% without compromising performance. The paper also proposes a unified framework for cross-layer optimization, demonstrating its potential via a representative diagram and data table.
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