perform resource accounting

This study addresses the lack of a systematic overview of open-source software energy measurement tools, which hinders energy-aware software design and tool selection. From a mining software repositories (MSR) perspective, the authors employ qualitative content analysis to screen and categorize 585 GitHub projects, identifying 24 high-quality open-source energy measurement tools. The work systematically characterizes these tools in terms of architectural design, measurement granularity—spanning from CPU-level to process, container, and AI workload levels—and their capabilities for carbon emission estimation. By elucidating evolutionary trends in tool development, this research provides software architects with a structured foundation and practical guidance for informed tool selection in energy-efficient software engineering.

Addressing the challenge of imprecise software resource consumption measurement—which hinders green computing advancement—this paper proposes the Green Metrics Tool (GMT) framework. GMT establishes a controlled, reproducible, full-lifecycle measurement infrastructure within containerized environments, integrating multi-source resource monitoring and standardized metric collection. Methodologically, it innovatively combines Life Cycle Assessment (LCA) principles with large language models (LLMs) to enable visual energy consumption analysis, cross-project comparability, and intelligent optimization recommendations driven jointly by rule-based logic and semantic understanding. GMT significantly enhances automation and engineering practicality in green software evaluation, delivering actionable energy-efficiency diagnostics and optimization pathways for developers. Empirical validation demonstrates its effectiveness in identifying high-energy-consumption components and supporting sustainability-oriented improvements.

This study addresses the disconnect between bottom-up energy consumption metrics and top-down reporting in cloud carbon accounting, which impedes unified accountability. To bridge this gap, we propose the Reconciled Software Carbon Intensity (rSCI) framework. By designing residual decomposition and allocation algorithms grounded in physical drivers and integrating cloud provider audit data, rSCI re-anchors bottom-level energy estimates to top-level emissions reports. The research exposes limitations in existing accounting methodologies and establishes standardized procedures for rSCI implementation. This approach effectively reconciles dual-source signals while preserving optimization incentives, enabling precise attribution of idle capacity and embodied carbon emissions. Ultimately, it offers a new paradigm for carbon accountability in cloud computing.

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.

In data-driven economies, organizations lack systematic frameworks for evaluating and managing data value within internal business processes. To address this gap, this study develops a comprehensive data value assessment framework grounded in the Balanced Scorecard’s internal process perspective, integrating three interrelated dimensions: data quality, governance compliance, and operational efficiency. It introduces a novel, multi-layered taxonomy of data value—spanning technological, organizational, and regulatory dependencies—that resolves metric redundancy and establishes cross-dimensional conceptual linkages. Through systematic literature review, theoretical modeling, indicator clustering, and taxonomy design, the research produces a scalable, reusable data value metrics system. This system underpins standardized data valuation models and decision-support systems, offering both a methodological foundation and actionable implementation pathways for cross-sectoral data assetization. (149 words)

为解决数据分析工程中治理、质量、来源和可重复性的问题，本文提出UnespDataLens-RM参考模型，通过整合技术操作过程和跨领域能力来提高分析流程的可靠性。

为解决工业能源数据采集问题，提出STREAM框架，通过目标驱动和不确定性评估方法确保数据满足能源性能评估需求。

This study addresses the absence of standardized carbon accounting and environmental assessment frameworks in artificial intelligence research. Through an automated literature review, it reveals the prevalent lack of environmental reporting in NeurIPS publications. Methodologically, this work proposes SMAJ, a novel framework designed to balance model accuracy with computational efficiency, thereby challenging conventional state-of-the-art paradigms. Furthermore, it develops a heuristic algorithm for carbon emission estimation alongside carbonbenchmark, a drop-in software tool for practical implementation. The primary contributions lie in defining standardized sustainability metrics for AI evaluation and providing deployable carbon tracking utilities. Ultimately, this research advocates for an approach wherein AI development simultaneously pursues performance breakthroughs and environmental responsibility.

This work addresses the significant limitations of spreadsheet-based analysis in reproducibility, auditability, version control, and automation. It proposes a migration pathway from Excel to research-grade analytical workflows by leveraging Python’s pandas library as a bridge. The study introduces an innovative set of Excel-to-pandas mapping rules, categorizes nine canonical workflow patterns, and compiles a catalog of common failure modes. Seven end-to-end real-world examples demonstrate the approach in practice. By retaining Excel as a familiar interface for input and output while integrating version control, automated refreshing, and seamless incorporation of statistical and machine learning methods, the proposed framework enables governed, reproducible, and auditable tabular data analysis.

This study addresses the challenge of inaccurate energy consumption estimation for distributed batch-processing applications like Apache Spark in cloud environments, where node-level hardware energy counters are typically inaccessible. Focusing on Apache Spark deployed on Kubernetes, the work presents the first systematic comparison between resource-utilization-based energy models and ground-truth measurements from Intel RAPL across both AWS bare-metal instances and on-premises clusters. It investigates the impact of CPU and memory utilization signals on estimation accuracy and introduces external monitoring to enhance model fidelity. Experimental results demonstrate that incorporating external monitoring significantly mitigates energy underestimation—reducing the error from −29.58% to −24.41% on AWS and from −24.00% to −16.22% in the local cluster—thereby validating its effectiveness in improving energy estimation accuracy.