cost accounting

AI infrastructure confronts multidimensional physical and economic constraints—including power, thermal management, water usage, interconnect bandwidth, memory capacity, and data throughput—while existing metrics (e.g., PUE, TCO) are siloed and fail to capture the coupled trade-offs among energy efficiency, performance, and cost, hindering cross-layer co-optimization. To address this, we propose a unified measurement architecture grounded in a 6×3 cross-layer taxonomy—spanning facility, network, compute, storage, software, and application layers, each annotated with physical, computational, and economic semantics—and introduce the Measurement Propagation Graph (MPG) to enable, for the first time, system-level, three-dimensional relational modeling. Leveraging systematic literature review, meta-analysis, and graph-based modeling, our framework integrates heterogeneous, multi-source metrics. It supports benchmarking, capacity planning, and total cost of ownership analysis, substantially enhancing interpretability of AI cluster efficiency frontiers and enabling rigorous multi-objective optimization.

Current environmental impact assessments of the AI lifecycle suffer from tool heterogeneity, insufficient coverage of water usage and value-chain stages, and poor cross-study comparability. To address these gaps, this paper proposes a unified operational definition of Green AI and introduces a five-stage lifecycle framework—encompassing hardware manufacturing, model development, training, deployment, and reuse. It explicitly distinguishes Green AI (energy-efficiency–focused) from Sustainable AI (holistically sustainable across environmental, social, and economic dimensions). Methodologically, the framework integrates Life Cycle Assessment (LCA), PDCA-based governance, edge–cloud co-optimized hardware strategies, and a calibrated multi-level metrics system combining estimation and empirical measurement. The resulting methodology enables vendor-agnostic, reproducible quantification of energy consumption, carbon emissions, water use, and embodied impacts. This significantly enhances transparency and comparability, providing researchers, engineers, and policymakers with an evidence-driven, actionable guide for sustainable AI development and deployment.

Current HPC billing mechanisms neglect energy consumption and carbon emissions, undermining user incentives for energy efficiency. To address this, we propose a dual-track transparent pricing mechanism—integrating both energy and carbon footprint metrics—and introduce the first end-user-oriented, multi-resource carbon-aware pricing paradigm, dynamically linking computational cost to real-time energy use and carbon emissions. Methodologically, we combine large-scale power consumption modeling and simulation, a prototype system integrating Slurm with real-time smart meters, and controlled user-behavior experiments complemented by surveys. Our empirical investigation uncovers root causes of weak energy-saving awareness among users and enables the design of an incentive-compatible sustainable computing economic model. Results demonstrate that the new mechanism reduces peak energy consumption by 12–19%, achieves billing accuracy with <3% error, and significantly increases users’ willingness to adopt energy-saving behaviors (p < 0.01).

This study addresses the scarcity of practical approaches for effectively measuring and reducing carbon emissions in real-world computing systems. Conducted on a public cloud platform, this work implements green software practices on an online production service by innovatively integrating the cost-driven mechanisms of serverless architectures with carbon emission estimation. Through energy-efficiency optimization techniques, the system’s carbon footprint is significantly reduced. The project demonstrates that cost optimization can serve as an effective indirect driver for improving energy efficiency. Furthermore, it distills a set of reusable and actionable principles for green software engineering, offering empirical insights and methodological support for sustainable software development in industrial settings.

This study addresses the common oversight of full lifecycle carbon emissions in hardware upgrade decisions by proposing a lifecycle-aware simulation framework. The framework uniquely integrates workload characteristics, location-specific time-varying grid carbon intensity, and multiple embodied carbon allocation strategies—such as uniform amortization and front-loading—with multi-generation CPU power models to dynamically evaluate the total carbon footprint of different deployment scenarios. Experimental results demonstrate that, particularly under low-utilization conditions or in regions with cleaner electricity grids, extending the operational lifespan of existing hardware can substantially reduce overall emissions. These findings challenge the prevailing assumption that newer hardware is inherently more environmentally sustainable and offer a novel paradigm for greener computing practices.

To address the high manual monitoring overhead, coarse-grained modeling, and insufficient integration of carbon intensity (CI) data in scientific workflow carbon footprint assessment, this paper proposes the first automated carbon estimation framework for Nextflow. The framework leverages native execution traces to eliminate manual power instrumentation, dynamically converts energy consumption to carbon emissions by fusing high- and low-resolution temporal CI data, and supports user-defined, CPU-frequency-aware fine-grained power models. Through resource-aware modeling and comparative evaluation against RAPL and GA methods, it achieves precise task-level decomposition of carbon emissions and energy consumption on two real-world Nextflow workflows. Experimental results demonstrate controlled estimation error and superior accuracy over state-of-the-art RAPL and GA approaches. Additionally, the framework enables sensitivity analysis with respect to CI granularity and CPU frequency parameters. The implementation is publicly available.

The ICT sector accounts for 2% of global carbon emissions, necessitating carbon-efficient software engineering; however, existing research is fragmented and lacks systematic integration. Method: We conduct the first systematic literature review (SLR) specifically targeting carbon-efficient software, analyzing 65 state-of-the-art studies through a 5W1H–based taxonomy. Contribution/Results: We propose a unified knowledge structure that rigorously defines the domain’s boundaries, identifies critical research gaps and practical challenges, and synthesizes reusable design guidelines, a standardized carbon footprint measurement framework, and a comprehensive taxonomy of carbon-reduction techniques. Furthermore, we articulate 12 open challenges. This work establishes the first theoretically grounded taxonomy and actionable roadmap for green software engineering, thereby bridging a critical gap in the integration of carbon-aware software development knowledge.

This study addresses the limited scope of traditional high-performance computing (HPC) evaluations, which typically focus solely on performance and energy consumption while overlooking the comprehensive environmental costs of operational configurations. The authors propose the first job-level unified accounting framework that integrates both operational and full life-cycle (embodied) carbon and water footprints. Leveraging life-cycle assessment methodologies, real-time runtime monitoring, and hardware manufacturing emission data, the framework enables fine-grained quantification of environmental impacts. The analysis reveals that increasing thread count generally reduces total environmental footprints, albeit with diminishing marginal returns; while carbon footprints are predominantly driven by operational phases, water footprints are largely dominated by embodied impacts. By jointly incorporating both footprint types at the job granularity, this work establishes a novel paradigm for assessing HPC sustainability.

This work addresses the challenge of resource allocation in geographically distributed and heterogeneous continuum computing infrastructures, where combinatorial explosion and limited generalization hinder effective deployment. To tackle this, the study introduces, for the first time, the pricing structures commonly found in Software-as-a-Service (SaaS) ecosystems into the resource allocation problem, formulating a unified, price-based representation of the configuration space. The authors propose PRIME, a pricing-aware analysis engine that efficiently searches for cost-optimal deployment configurations satisfying both functional and non-functional constraints. Leveraging synthetic infrastructure topologies and workload generation techniques, the project constructs a comprehensive dataset comprising 9,600 diverse scenarios, demonstrating that the proposed approach achieves both scalability and computational efficiency in complex, heterogeneous environments.

This study addresses a critical gap in current AI efficiency evaluations, which typically focus only on isolated training or inference phases and fail to capture the full lifecycle resource consumption and environmental impact of AI systems. To overcome this limitation, the work introduces, for the first time, a comprehensive Life Cycle Assessment (LCA) framework tailored to machine learning. This approach systematically integrates energy use and embedded environmental costs across all stages—including hardware manufacturing, model training, and deployment—thereby transcending the narrow scope of conventional assessments. By providing a holistic and accurate methodology for evaluating sustainability, the proposed framework offers researchers, developers, and policymakers a robust tool to guide more environmentally responsible design, deployment, and regulation of AI technologies.

This work addresses the challenge of balancing emission constraints, operational cost, and service quality in dynamic power grids where carbon intensity varies over time. Traditional fixed emission rate strategies prove inadequate under such conditions. To overcome this limitation, the authors propose a time-window-based emission budgeting mechanism that replaces static rates, enabling applications to accrue emission allowances during low-carbon periods and flexibly consume them during high-carbon intervals. Integrated within a MAPE-K adaptive control architecture, the approach leverages real-time monitoring of grid carbon intensity and system power consumption to dynamically schedule resources while adhering to long-term emission caps. Simulations using six weeks of real-world data from Germany, France, and Poland demonstrate that the method improves task completion rates by up to 36% in volatile grids while matching the performance of existing approaches in stable grids, achieving effective co-optimization of emissions, cost, and performance.

This work addresses the challenge of accurately quantifying the carbon footprint of scientific workflows in shared virtualized environments, where existing tools rely on oversimplified power models and lack precision. We propose the first high-fidelity carbon footprint estimation framework that supports multi-cluster deployments and is extensible across diverse workflow systems, including Nextflow and Apache Airflow. Our approach integrates workflow execution traces, node-level fitted power models, hardware-level energy measurements via Intel RAPL, and time-aligned grid carbon intensity data, while accounting for operational emissions, embodied carbon, and water–land resource consumption. Experimental evaluation across three clusters demonstrates an average energy estimation error of only 10.8%, substantially outperforming current tools such as nf-core co2footprint, and confirms successful cross-platform deployment.