google-cloud-waf-reliability

SKILL.md

Google Cloud Well-Architected Framework skill for the Reliability pillar

Overview

The Reliability pillar of the Google Cloud Well-Architected Framework provides
principles and recommendations to help you design, deploy, and manage reliable,
resilient, and highly available workloads in Google Cloud. A reliable system
consistently performs its intended functions under defined conditions, is
resilient to failures, and recovers gracefully from disruptions, thereby
minimizing downtime, enhancing user experience, and ensuring data integrity.

Core principles

The recommendations in the reliability pillar of the Well-Architected Framework
are aligned with the following core principles:

Define reliability based on user-experience goals: Measurement of
reliability should reflect the actual experience of the system's users rather
than merely relying on infrastructure metrics. Focus on outcomes that matter
most to users. Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/define-reliability-based-on-user-experience-goals.md.txt

Set realistic targets for reliability: Determine appropriate Service
Level Objectives (SLOs) that balance the cost and complexity of maximizing
availability against business requirements. Utilize error budgets to manage
feature velocity. Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/set-targets.md.txt

Build highly available systems through resource redundancy: Eliminate
single points of failure by duplicating critical components across zones and
regions to maintain operations during localized outages. Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/build-highly-available-systems.md.txt

Take advantage of horizontal scalability: Design system architectures to
scale horizontally (adding more instances) to seamlessly accommodate load
fluctuations and improve overall fault tolerance. Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/horizontal-scalability.md.txt

Detect potential failures by using observability: Implement thorough
monitoring, logging, and alerting systems to proactively detect, diagnose,
and address anomalies before they cause user-facing issues. Grounding
document:
https://docs.cloud.google.com/architecture/framework/reliability/observability.md.txt

Design for graceful degradation: Architect systems to maintain critical
functionality, even if at reduced performance or with limited features, when
dependencies fail or the system experiences extreme stress. Grounding
document:
https://docs.cloud.google.com/architecture/framework/reliability/graceful-degradation.md.txt

Perform testing for recovery from failures: Build confidence in system
resilience by continuously simulating failures and verifying the
effectiveness of automated and manual recovery procedures. Grounding
document:
https://docs.cloud.google.com/architecture/framework/reliability/perform-testing-for-recovery-from-failures.md.txt

Perform testing for recovery from data loss: Regularly test backup and
restore protocols to ensure rapid recovery from data corruption or loss,
remaining within the defined Recovery Time Objective (RTO) and Recovery Point
Objective (RPO). Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/perform-testing-for-recovery-from-data-loss.md.txt

Conduct thorough postmortems: Foster a blameless culture by investigating
outages comprehensively to understand root causes, followed by implementing
measures that prevent recurrence. Grounding document:
https://docs.cloud.google.com/architecture/framework/reliability/conduct-postmortems.md.txt

Relevant Google Cloud products

The following are examples of Google Cloud products and features that are
relevant to reliability:

Compute: Compute Engine Managed Instance Groups (MIGs), Google Kubernetes
Engine (GKE), Cloud Run

Networking: Cloud Load Balancing, Cloud CDN, Cloud DNS

Storage and databases: Cloud Storage (multi-region), Cloud SQL High
Availability, Spanner, Filestore, Firestore

Operations: Cloud Monitoring, Cloud Logging, Google Cloud Managed Service
for Prometheus

Disaster recovery: Backup and DR Service, Filestore backups

Workload assessment questions

Ask appropriate questions to understand the reliability-related requirements and
constraints of the workload and the user's organization. Choose questions from
the following list:

How does your organization define and measure the reliability of your systems
in relation to user experience?

How does your organization approach setting reliability targets for your
services?

What is your organization's strategy for ensuring high availability through
resource redundancy?

How does your organization leverage horizontal scalability to maintain
performance and reliability?

How does your organization utilize observability (metrics, logs, traces) to
gain insights and detect potential failures?

How does your organization manage alerting based on observability data to
ensure timely responses to significant issues without causing alert fatigue?

What measures does your organization take to ensure systems can gracefully
degrade during high load or partial failures?

How frequently and comprehensively does your organization test for recovery
from system failures (e.g., regional failovers, release rollbacks)?

What is your organization's approach to testing for recovery from data loss?

How does your organization conduct and utilize postmortems after incidents?

Validation checklist

Use the following checklist to evaluate the architecture's alignment with
reliability recommendations:

User-focused SLIs and SLOs are explicitly defined and actively monitored.

The architecture avoids single points of failure through cross-zone or
cross-region redundancy.

Autoscaling is enabled to handle variable demand without manual intervention.

Application and infrastructure health checks are configured to trigger
automated failovers.

Regular backup schedules are in place, and restoration processes are routinely
tested.

The system architecture incorporates patterns like circuit breakers, retries
with exponential backoff, and rate limiting to support graceful degradation.

Game days or chaos engineering practices are regularly held to validate
failure recovery.

A formalized, blameless postmortem process exists to ensure organizational
learning from operational incidents.