Google Cloud Well-Architected Framework skill for the Sustainability pillar

Overview

Core principles

• Shared responsibility: Define the boundaries of responsibility and embrace a shared fate model, working with your cloud provider and partners to achieve optimal environmental outcomes for the entire ecosystem. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability#shared-responsibility
• Use regions that consume low-carbon energy: Prioritize Google Cloud regions with a high percentage of Carbon-Free Energy (CFE) and "Low CO2" indicators to lower the gross carbon emissions of your deployments. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/low-carbon-regions
• Optimize AI and ML workloads: Maximize computations per watt by matching algorithmic needs to specialized hardware (like TPUs) and applying mathematical techniques to reduce computational complexity. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/ai-ml-energy-efficiency
• Optimize resource usage: Eliminate energy waste by scaling resources to zero when idle, rightsizing virtual machines, and prioritizing managed services that dynamically match actual demand. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/optimize-resource-usage
• Develop energy-efficient software: Design your applications to minimize unnecessary CPU, memory, and network activity on both backend servers and end-user devices by using event-driven logic and optimized assets. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/energy-efficient-software
• Optimize data and storage: Reduce the environmental footprint of your storage by implementing lifecycle management to archive cold data and eliminating "dark data" that provides no business value. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/optimize-storage
• Continuously measure and improve: Gain visibility into your carbon emissions by analyzing granular data, identifying hotspots, and taking proactive steps to remediate inefficiencies. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/continuously-measure-improve
• Promote a culture of sustainability: Embed sustainability into your organizational governance, connect technical decisions to environmental goals, and ensure staff have the skills to implement green practices. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/culture
• Align sustainability practices with industry guidelines: Ensure that your sustainability initiatives are aligned with industry guidelines for measurement, reporting, and verification, such as W3C Web Sustainability Guidelines, Green Software Foundation, and Greenhouse Gas Protocol. Grounding document: https://docs.cloud.google.com/architecture/framework/sustainability/industry-guidelines

Relevant Google Cloud products

• 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.

Workload assessment questions

• 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?

Validation checklist

• 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.