Problem
Research questions and friction points this paper is trying to address.
Energy-Aware Computing
High-Performance Computing
Exascale
Generative AI
Green Computing
Energy-Aware Computing in the Year 2026
🤖 AI Summary
High energy consumption has emerged as a critical bottleneck in cloud computing, edge computing, and supercomputing systems supporting large-scale AI applications, necessitating urgent responses to challenges posed by energy use, carbon footprints, and power constraints. This work presents a systematic review of recent advances in energy-efficient computing and introduces the first multidimensional taxonomy that integrates hardware-software co-design, task scheduling, dynamic voltage and frequency scaling, workload consolidation, federated learning, and advanced cooling techniques. Emphasizing the paradigm shift toward green computing in integrated cloud-edge-supercomputing infrastructures driven by large AI models, this study offers a comprehensive roadmap for sustainable computing under carbon constraints, thereby advancing the ICT sector toward greater energy efficiency and lower emissions.
📝 Abstract
High-Performance Computing (HPC) has recently entered the Exascale era, and considerable efforts are being made to fully harness this potential power for large-scale applications, such as cutting-edge generative AI (training and exploitation). The corresponding energy consumption is very high, and forecasts are alarming, making this metric a critical systemic bottleneck. Addressing this issue presents a genuine challenge for the entire cloud-edge-HPC continuum at all scales, from low-power IoT microcontrollers to multi-megawatt data centers. Beyond financial costs, green computing is driven by considerations related to climate change and environmental concerns such as carbon footprint ($CO_2e$), as well as constraints on energy production and supply, leading to a real need to regulate {\em information and communication technology} (ICT) activities. This article presents a comprehensive overview of energy-efficient computing, taking into account the most recent and significant contributions. Based on this exploration of the state of the art, we design and describe a holistic taxonomy of the aforementioned publications, structured around various perspectives, including {\em hardware and software aspects, measurement instrumentation, software optimizations, dynamic task scheduling, voltage scaling, workload consolidation, federated learning}, and {\em cooling}. Particular emphasis is placed on large-scale AI, which receives significant attention due to its considerable resource requirements. We conclude with an analysis of a forward-looking roadmap that considers the main perspectives of sustainable computing.
Innovation
Methods, ideas, or system contributions that make the work stand out.
energy-aware computing
holistic taxonomy
exascale AI
dynamic voltage scaling
federated learning
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