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Google Cloud Well-Architected Framework skill for the Sustainability pillar

面向可持续性支柱的Google Cloud架构完善框架技能

Overview

概述

The Sustainability pillar of the Google Cloud Well-Architected Framework provides principles and recommendations to help you minimize the environmental impact of your cloud workloads. It focuses on a shared responsibility model—Google optimizes the sustainability of the cloud, while customers optimize sustainability in the cloud. By making informed decisions about architecture, resource allocation, and region selection, you can significantly reduce your carbon footprint and improve overall energy efficiency.
Google Cloud架构完善框架的可持续性支柱提供了原则与建议，帮助你最大限度降低云工作负载对环境的影响。它聚焦于共享责任模式——Google优化云本身的可持续性，而客户则优化在云中的可持续性。通过在架构、资源分配和区域选择上做出明智决策，你可以显著减少碳足迹并提升整体能源效率。

Core principles

核心原则

The recommendations in the sustainability pillar of the Well-Architected Framework are aligned with the following core principles:
架构完善框架可持续性支柱中的建议与以下核心原则保持一致：

Relevant Google Cloud products

相关Google Cloud产品

The following are examples of Google Cloud products and features that are relevant to sustainability:
  • Visibility and measurement:
    • Carbon Footprint: Provides dashboard visibility into greenhouse gas emissions associated with Google Cloud usage.
    • BigQuery: Analyzes exported Carbon Footprint data alongside billing data to identify emission hotspots.
  • Infrastructure and operations:
    • Google Cloud Region Picker: Helps weigh carbon footprint, cost, and latency when selecting deployment locations.
    • Active Assist / Recommender: Automatically identifies idle resources and provides VM rightsizing recommendations to reduce waste.
    • Cloud Run / GKE Autopilot: Fully managed compute environments that optimize cluster usage and can scale to zero when idle.
    • Cloud Batch: Optimizes the scheduling of batch jobs, allowing execution during periods of high Carbon-Free Energy.
    • Spot VMs: Utilizes unused data center capacity for fault-tolerant workloads, improving overall hardware efficiency.
  • Data and AI:
    • Cloud Storage Lifecycle Management: Automatically transitions older data to lower-energy storage classes (Nearline, Coldline, Archive).
    • Cloud TPUs: Specialized hardware optimized for the energy efficiency of large-scale AI/ML matrix multiplications.
以下是与可持续性相关的Google Cloud产品和功能示例：
  • 可见性与测量：
    • Carbon Footprint：提供仪表盘，展示与Google Cloud使用相关的温室气体排放情况。
    • BigQuery：将导出的Carbon Footprint数据与账单数据结合分析，识别排放热点区域。
  • 基础设施与运维：
    • Google Cloud Region Picker：在选择部署位置时，帮助权衡碳足迹、成本和延迟。
    • Active Assist / Recommender：自动识别空闲资源，并提供虚拟机规格调整建议以减少浪费。
    • Cloud Run / GKE Autopilot：全托管计算环境，优化集群使用率，空闲时可缩容至零。
    • Cloud Batch：优化批处理作业调度，允许在无碳能源占比高的时段执行。
    • Spot VMs：利用数据中心未使用的容量运行容错工作负载，提升整体硬件效率。
  • 数据与AI：
    • Cloud Storage Lifecycle Management：自动将旧数据转换为低能耗存储类别（Nearline、Coldline、Archive）。
    • Cloud TPUs：专为大规模AI/ML矩阵乘法的能源效率优化的专用硬件。

Workload assessment questions

工作负载评估问题

Ask appropriate questions to understand the sustainability-related requirements and constraints of the workload and the user's organization. Choose questions from the following list:
  • Cloud sustainability:
    • How do you define the boundaries of sustainability responsibility between your organization and your cloud provider?
    • How do you leverage cloud capabilities and AI to drive sustainability outcomes for your broader business operations?
    • How does your cloud strategy account for the sustainability impact of your partner ecosystem and multi-cloud environments?
  • Use regions that consume low-carbon energy:
    • How do you incorporate carbon intensity into your Google Cloud region selection strategy?
  • Optimize AI and ML workloads:
    • How do you optimize the energy efficiency of your AI and machine learning lifecycles?
  • Optimize resource usage:
    • How do you ensure your infrastructure footprint dynamically matches actual workload demand?
    • How do you select and maintain the hardware types used for your cloud workloads?
    • What is your strategy for handling non-urgent or compute-intensive background tasks?
    • How do you balance the need for high availability and disaster recovery with sustainability?
  • Develop energy-efficient software:
    • How do you ensure your backend logic minimizes unnecessary CPU, memory, and network activity?
    • How do you manage the overall efficiency and maintenance of your codebase for sustainability?
    • How do you minimize the data volume and processing load that your application places on end-user devices?
    • How does your user experience (UX) design contribute to energy efficiency for the end user?
  • Optimize data and storage:
    • What process do you have for managing the environmental footprint of your data and storage?
  • Continuously measure and improve:
    • How do you analyze your carbon data to prioritize optimization efforts?
    • How is sustainability measurement embedded into your organization’s governance and culture?
    • What is your current process for gaining visibility into your cloud-related carbon emissions?
    • What proactive steps do you take to remediate identified carbon hotspots?
  • Promote a culture of sustainability:
    • How do you connect individual technical decisions to the organization's mission and hold teams accountable for results?
    • How do you ensure your technical and business staff have the specific skills required to implement sustainability practices?
提出合适的问题，了解工作负载及用户组织在可持续性方面的需求与约束。可从以下列表中选择问题：
  • 云可持续性：
    • 你如何定义组织与云服务商之间的可持续性责任边界？
    • 你如何利用云能力和AI推动更广泛业务运营的可持续性成果？
    • 你的云策略如何考虑合作伙伴生态系统和多云环境的可持续性影响？
  • 使用低碳能源区域：
    • 你如何将碳强度纳入Google Cloud区域选择策略？
  • 优化AI与ML工作负载：
    • 你如何优化AI和机器学习生命周期的能源效率？
  • 优化资源使用：
    • 你如何确保基础设施规模动态匹配实际工作负载需求？
    • 你如何选择和维护云工作负载使用的硬件类型？
    • 你针对非紧急或计算密集型后台任务的策略是什么？
    • 你如何平衡高可用性、灾难恢复需求与可持续性？
  • 开发节能软件：
    • 你如何确保后端逻辑最大限度减少不必要的CPU、内存和网络活动？
    • 你如何管理代码库的整体效率和维护以实现可持续性？
    • 你如何最大限度减少应用对终端用户设备造成的数据量和处理负载？
    • 你的用户体验（UX）设计如何为终端用户提升能源效率？
  • 优化数据与存储：
    • 你有哪些管理数据和存储环境影响的流程？
  • 持续测量与改进：
    • 你如何分析碳数据以确定优化工作的优先级？
    • 可持续性测量如何融入组织的治理和文化？
    • 你当前获取云相关碳排放可见性的流程是什么？
    • 你采取哪些主动措施整改已识别的碳热点区域？
  • 推广可持续文化：
    • 你如何将个人技术决策与组织使命关联，并让团队对结果负责？
    • 你如何确保技术和业务人员具备实施可持续实践所需的特定技能？

Validation checklist

验证清单

Use the following checklist to evaluate the architecture's alignment with sustainability recommendations:
  • Cloud sustainability:
    • The organization embraces a shared responsibility and shared fate model for sustainability.
    • AI is used as a catalyst for profitability and resilience to streamline operations, or sustainability is integrated into the design process to create positive feedback loops.
    • Collaborations with sustainable partners are prioritized and multi-cloud data portability is leveraged, or internal practices align with recognized global standards like the Green Software Foundation.
  • Use regions that consume low-carbon energy:
    • A data-driven policy prioritizes regions with high Carbon-Free Energy (CFE%) and "Low CO2" indicators, or the Google Cloud Region Picker is actively used to balance carbon footprint with cost and latency.
  • Optimize AI and ML workloads:
    • Algorithmic needs are matched to specialized hardware (TPUs) to maximize computations per watt, or mathematical techniques like model compression and PEFT are applied to reduce computational complexity.
  • Optimize resource usage:
    • Fully managed services that scale to zero when idle are utilized, or Horizontal Pod Autoscaling (HPA) and Vertical Pod Autoscaling (VPA) are used in GKE to prevent over-provisioning.
    • A formal process exists to upgrade to the newest machine types for improved performance-per-watt, or workloads are actively matched to specialized machine families.
    • Batch jobs are proactively scheduled to run during periods or in regions with the highest proportion of CFE, or Spot VMs are utilized for non-critical batch jobs.
    • "Cold DR" or serverless failover is prioritized to ensure secondary regions remain at zero energy consumption until an event occurs, or Infrastructure as Code (IaC) is used to rapidly provision a recovery environment only when needed.
  • Develop energy-efficient software:
    • Resource-intensive busy loops or constant polling are replaced with event-driven logic, or algorithms with optimal time complexity and data structures are prioritized.
    • The "Don't Repeat Yourself" (DRY) principle is adhered to with regular refactoring, or intelligent caching (e.g., Memorystore) is implemented with smart eviction policies.
    • The download size of website products is measured and maintained against a strict budget, or CI/CD pipelines automate the minimization and compression of HTML, CSS, and JS files.
    • Static sites or Progressive Web Apps (PWAs) are preferred for faster loading, or DOM manipulation is minimized to reduce device power consumption.
  • Optimize data and storage:
    • Object Lifecycle Management is used to automatically move cold data to Archive storage, or discovery techniques (e.g., Dataplex) are used to identify and eliminate "dark data".
  • Continuously measure and improve:
    • Carbon data is analyzed by project, region, and service to identify gross emitters, or carbon data is joined with Billing data in BigQuery to correlate cost and environmental impact.
    • A formal GreenOps function defines accountability for carbon reduction targets, or verified Carbon Footprint data from BigQuery supports formal ESG disclosures.
    • Applications are instrumented to measure the specific carbon intensity of software features, or automated exports of Carbon Footprint data to BigQuery are configured for deep analysis.
    • The unattended project recommender and Active Assist are regularly used to decommission idle resources, or proactive projects re-architect hotspots by shifting workloads to low-carbon regions.
  • Promote a culture of sustainability:
    • Abstract carbon metrics are transformed into tangible progress indicators in annual reports, or sustainability is treated as a first-class technical requirement (NFR) tied to KPIs and performance reviews.
    • Training tailored to specific job roles (e.g., developers on code efficiency, FinOps on carbon unit economics) is provided, or teams are formally trained to access and interpret carbon footprint data.
使用以下清单评估架构与可持续性建议的契合度：
  • 云可持续性：
    • 组织采用可持续性的共享责任和共享命运模式。
    • AI被用作提升盈利能力和韧性的催化剂以简化运营，或可持续性被整合到设计流程中以创建正向反馈循环。
    • 优先与可持续合作伙伴协作，并利用多云数据可移植性，或内部实践与绿色软件基金会等公认全球标准对齐。
  • 使用低碳能源区域：
    • 采用数据驱动政策优先选择高无碳能源占比（CFE%）和“低CO₂”指标的区域，或主动使用Google Cloud Region Picker平衡碳足迹与成本、延迟。
  • 优化AI与ML工作负载：
    • 将算法需求与专用硬件（TPUs）匹配以最大化每瓦特计算量，或应用模型压缩、PEFT等数学技术降低计算复杂度。
  • 优化资源使用：
    • 使用空闲时可缩容至零的全托管服务，或在GKE中使用Horizontal Pod Autoscaling（HPA）和Vertical Pod Autoscaling（VPA）防止过度配置。
    • 存在正式流程升级至最新机型以提升每瓦特性能，或主动将工作负载与专用机型系列匹配。
    • 主动调度批处理作业在无碳能源占比最高的时段或区域运行，或使用Spot VMs运行非关键批处理作业。
    • 优先选择“冷灾备”或无服务器故障转移，确保次级区域在事件发生前保持零能耗，或使用Infrastructure as Code（IaC）仅在需要时快速部署恢复环境。
  • 开发节能软件：
    • 用事件驱动逻辑替代资源密集型循环轮询，或优先选用具有最优时间复杂度和数据结构的算法。
    • 遵循“Don't Repeat Yourself”（DRY）原则并定期重构，或实现智能缓存（如Memorystore）并采用智能淘汰策略。
    • 测量并严格控制网站产品的下载大小，或通过CI/CD流水线自动最小化和压缩HTML、CSS及JS文件。
    • 优先选择静态站点或Progressive Web Apps（PWAs）以实现更快加载，或最小化DOM操作以降低设备功耗。
  • 优化数据与存储：
    • 使用对象生命周期管理自动将冷数据迁移至Archive存储，或使用Dataplex等发现技术识别并消除“暗数据”。
  • 持续测量与改进：
    • 按项目、区域和服务分析碳数据以识别主要排放源，或在BigQuery中将碳数据与账单数据关联以对比成本与环境影响。
    • 设立正式的GreenOps职能部门明确碳减排目标的责任，或使用BigQuery中经过验证的Carbon Footprint数据支持正式ESG披露。
    • 对应用进行 instrumentation 以测量软件功能的特定碳强度，或配置自动将Carbon Footprint数据导出至BigQuery进行深度分析。
    • 定期使用无人值守项目推荐器和Active Assist来停用空闲资源，或通过主动项目将工作负载转移到低碳区域以重构热点区域。
  • 推广可持续文化：
    • 将抽象的碳指标转化为年度报告中的切实进展指标，或将可持续性视为与KPI和绩效评估挂钩的一等技术需求（NFR）。
    • 提供针对特定岗位的培训（如面向开发者的代码效率培训、面向FinOps的碳单位经济培训），或对团队进行正式培训以获取和解读碳足迹数据。