google-cloud-waf-sustainability
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.
What this skill does
# 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?
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