施耐德电气

Data centers are energy-intensive infrastructures that generate, manage, and store information for our interconnected society. Models based on Artificial Intelligence (AI), such as ChatGPT, are increasingly accessible, leading to significant energy consumption and associated carbon emissions. Assessing the environmental footprint of Generative AI (GenAI) is essential for evaluating its sustainability and promoting responsible AI development. In this work, a comprehensive environmental assessment of GenAI systems was performed - which includes both training and inference phases - using a life cycle assessment (LCA) approach. Prior studies have primarily focused on server-level assessments or energy consumption analyses. In contrast, this work considers the full lifecycle of data centers and evaluates environmental impacts across complete architectural configurations, offering a broader and more integrated perspective. Finally, multiple data center architectures are compared, from edge systems to AI dedicated infrastructures. Two simulation-based use cases are presented: (1) A 20-year simulation comparing different data center architectures across three indicators - total emissions, emissions per year, and emissions per installed IT MW. For a subset of these architectures, emissions per Floating-Point Operations Per Second (FLOPS) are also included to assess performance efficiency - considering that FLOPS estimations can only be done on GPU based data center architectures; (2) A focused simulation comparing the environmental footprint of three large language models - GPT-4o, LLaMA 3.1405B, and DeepSeek V3 - to quantify trade-offs between benchmark performance and environmental impact. By expanding the scope of assessment and incorporating varied use cases, this work aims to inform strategies for minimizing the environmental costs of GenAI while advancing sustainable AI development.

Agile teams often struggle to translate business requirements into maintainable, high-quality software due to the persistent ambiguity in the roles and relationships of behavior-driven development (BDD), Acceptance Test-driven Development (ATDD), and Test-driven Development (TDD). These approaches are frequently misunderstood, inconsistently applied, and only loosely connected within a unified delivery lifecycle. This article introduces BLOW (Behavior-Driven Development in a Layered Organization of Work-Centers), a systematic approach that establishes BDD as the coordinating methodology between ATDD (business-focused) and TDD (technology-focused). BLOW structures scenario-driven development across layered domains of accountability with clearly defined roles and responsibilities, organizing delivery through nested work-centers that transform user stories into executable specifications and production code. This approach integrates two complementary collaboration practices: the Three Amigos for discovering and formulating business scenarios, and the proposed Technical Three Amigos for linking those scenarios to Technical Domain Contexts, identifying required Enablers, and deriving technical scenarios when additional architectural support is needed. The proposed operating model emphasizes observability through executable scenarios as first-class artifacts, introducing native, test-anchored metrics that support reasoning about progress, technical effort, and value delivery within scenario-driven development. An exploratory longitudinal case study, consisting of a single-sprint proof of concept followed by an 18-month production deployment, reports patterns in which technical enablement precedes business value delivery and reusable infrastructure supports sustained growth of business scenarios over time. The findings also indicate that changes in the applied operating model are associated with measurable shifts in scenario evolution and internal quality indicators. Overall, BLOW provides a governance-compatible, end-to-end approach for organizing scenario driven development and improving alignment between stakeholder intent and technical implementation in complex software systems.

SUMMARY & CONCLUSIONSCooling systems—particularly liquid cooling—are a critical mission segment in data centers. Any malfunction or operational failure in these systems can lead to substantial Data centers operational disruptions. To mitigate such risks, proactive Prognostics and Health Management (PHM) offers a promising framework for early anomaly detection and estimation of the system’s Remaining Useful Life (RUL). Various methodologies exist for collecting and analyzing health data to support PHM, ranging from sensor-based monitoring to advanced data analytics and machine learning techniques. Foundation models—large-scale, pre-trained neural networks developed on vast datasets —offer transformative potential for enabling data-driven PHM applications. These models deliver scalable and adaptable solutions that can be fine-tuned for specific tasks, accelerating innovation and significantly reducing the time and resources required to develop AI-powered systems. The model also performs well when health data from the system is limited, making it suitable for data-constrained environments.This study presents a novel approach to PHM in liquid-cooled data center environments by leveraging foundation models for time-series analysis. Liquid cooling is increasingly adopted in high-performance computing due to its superior thermal efficiency over traditional air-based cooling systems. However, the complexity of liquid cooling infrastructure and the cost of PHM implementation—particularly sensor deployment, data availability, data processing, and model training—pose significant challenges to broader adoption.To address the challenges of implementing PHM in liquid-cooled data centers, it is crucial to optimize sensor placement, standardize data formats for efficient storage and transmission, enhance data processing capabilities, and ensure customer data privacy [1]. This study focuses on the data processing aspect by proposing the use of foundation models as a modular feature extraction backbone for PHM tasks. These models excel at capturing rich temporal patterns from sensor data and exhibit strong generalization performance, even when trained on limited labeled or unlabeled datasets. Specifically, we employ Chronos-Bolt, a transformer-based foundation model for time-series forecasting, which is adapted to process continuous signals using quantization and tokenization techniques. This approach significantly reduces the need for manual feature engineering and large-scale domain-specific training.The degradation process and abnormal operation due to random failures in the data center cooling system is analyzed through a simulated model in Simulink. The proposed methodology is also validated using an HVAC dataset from the Lawrence Berkeley National Laboratory (LBNL). The current research is focused on the chiller component, which is central to energy consumption and thermal regulation. In our case study, the model successfully detected faults and classified different severity levels using a set of selected relevant input features.The dataset encompasses various failure types, including sensor bias, leakage, and fouling of cooling system components. In particular, several measurement biases are introduced to the temperature sensors on the chiller, and the corresponding operating conditions are simulated to reflect these anomalies. Experimental results demonstrate that the proposed method effectively detects sensor bias faults and accurately estimates fault intensity, with especially strong performance in identifying positive biases. Additionally, a feature impact analysis was conducted to evaluate the influence of selected input variables on model performance. The observed dependencies provide valuable insights for optimizing feature selection across different operational scenarios.In conclusion, this work demonstrates the practicality and effectiveness of using foundation models for PHM in liquid cooling systems, even under data-scarce conditions. The modularity, low data requirements, and adaptability of this approach make it well-suited for real-world deployment in data centers. Future work will explore broader fault types, richer sensor sets, and integration with edge computing to enable real-time health monitoring and control.

Programmable-payment protocols such as x402 enable per-request micropayments, but they do not determine which payable service an autonomous agent should buy under a finite wallet. We formulate this buyer-side problem as agent-native payment decision-making: contextual provider selection under wallet pressure, chosen-only paid feedback, and changing market conditions. We propose 402Pilot, a protocol-agnostic buyer-side decision layer between autonomous agents and payment execution that implements purchasing policies for selecting among payable providers. We instantiate it with PA-DCT, a payment-aware discounted contextual Thompson-sampling policy that adapts purchasing decisions under wallet pressure while learning from post-payment feedback. To evaluate buyer-side payment policies, we introduce 402Pilot-Bench, a frozen-replay benchmark spanning 823 tasks, five heterogeneous provider pipelines, and three market regimes, each evaluated over 30 paired seeds. PA-DCT achieves the strongest fixed-wallet adaptive trade-off among non-oracle policies: it maintains competitive service quality while spending only 39 to 43 percent of the wallet and reallocates spending as market conditions change. It attains the best non-oracle PA-gap/T under the price shock and the best mean and worst-case ranks across the nine scenario-metric combinations of quality, ROI, and PA-gap/T. Comparisons with learning baselines and component ablations further support the effectiveness and design of the proposed decision policy. These results suggest that programmable payment must be complemented by buyer-side decision-making capable of learning service value and adapting purchasing decisions accordingly.

SUMMARYAdopting Agile approach in product design process became necessary for addressing the challenges and limitations of the traditional design methods and to remain competitive in today's dynamic and fast-paced market. By implementing Agile iterative design cycles based on Risk-based Design for Safety and Reliability Plans and utilizing Agile tools, organizations can drive innovative and continuous product design improvement. This approach enables product designs to adapt to the continuous evolving market requirements, while ensuring all potential risks are early detected with preventive mitigations for a safe, robust and reliable product design.This paper presents a comprehensive Agile methodology framework for hardware product design; explaining the key elements of deployment: (i) Strategy, (ii) Structure, (iii) Process, (iv) People, and (v) Technology. In addition to demonstration of the methodology illustrated through a practical case study for product design in agile using an example of a Capacitor Bank Sub-system that is part of Variable Frequency Drive (VFD) System (Power Electronic System). The Agile design process includes: (i) Preliminary Risk Assessment, (ii) Development of the Risk-Based Design for Safety and Reliability Plan, iii) Execution of the Plan within Agile Iterations (Sprints). Finally concludes with the benefits as well as challenges with recommendations on how to overcome them on applying Agile product design with Risk-Based Design for Safety and Reliability Plan.The benefits of applying agile methodology in hardware engineering go beyond the traditional companies’ performance metrics, to include the reduced time-to-market, less quality problems, and complexity issues, and improved productivity, predictability, as well as continuous design evolution adopted to market requirements.The full Agile transformations may span several years, yet agile pilot projects can deliver fast visible results that proves the shift is worth doing that builds the momentum and reinforce engineers buy in.