--- name: google-cloud-waf-sustainability description: >- Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud. --- # Google Cloud Well-Architected Framework skill for the Sustainability pillar ## Overview 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. ## Core principles The recommendations in the sustainability pillar of the Well-Architected Framework are aligned with the following 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 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. ## Workload assessment questions 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? ## Validation checklist 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.