Implement Data Resiliency and Replication Strategies
Data Resiliency ensures data survives a regional disaster. Managed services like Cloud Storage, Firestore, and Spanner offer multi-regional options that automatically replicate data across geographically separate areas. Cloud Storage buckets can be configured as Dual-region or Multi-region for the highest protection, with features like turbo replication guaranteeing a specific RPO. Databases like Cloud SQL use Cross-region Replicas that can be promoted if the primary region fails. Automated backups and Point-in-Time Recovery (PITR) protect against data corruption, while retention policies manage how long backup data is kept.
Orchestrate Traffic Management and Failover Mechanisms
Traffic management and automated failover minimize manual recovery steps to meet RTO and RPO goals. Global Cloud Load Balancing uses continuous health checks to monitor backend servers and automatically reroutes traffic to healthy instances in another location if failures occur. Cloud DNS adds control with configurable routing policies, such as failover routing, to switch users to a backup IP address. Managed Instance Groups (MIGs) maintain identical virtual machines across zones and autoheal failed instances. A multi-region architecture, replicating data and application resources across global regions, provides maximum availability against regional disasters.
Cost optimization
Resource Governance and Rightsizing Operations
Rightsizing means adjusting cloud resources to match actual demand to avoid paying for unused capacity. Tools like Cloud Monitoring and the Recommender API identify underutilized assets and recommend scaling them down. Autoscaling, using services like Compute Engine Managed Instance Groups (MIGs), automatically adjusts capacity based on real-time traffic. Pricing models like Committed Use Discounts (CUDs) offer lower rates for steady workloads, while Spot VMs provide major savings for fault-tolerant tasks. Cost allocation uses labels and Cloud Billing reports to track spending by department, and budgets with alerts prevent unexpected spending spikes.
Storage Class and Data Lifecycle Management
Google Cloud Storage offers several classes balancing performance and cost: Standard for frequent access, and Nearline, Coldline, and Archive for cheaper, less frequent access. Object Lifecycle Management automates data handling by creating policies that transition objects to cheaper classes or delete them when no longer needed. The Autoclass feature simplifies this by automatically moving objects between storage classes based on access patterns. Cost optimization also involves moving data from high-performance systems like Filestore to Cloud Storage after processing. Using labels and monitoring tools ensures lifecycle policies meet business goals and control costs.
Strategic Pricing and Discount Models
Strategic pricing uses discount models to lower costs. Committed Use Discounts (CUDs) provide lower rates for committing to resource use for one or three years, ideal for predictable workloads. Spot VMs offer up to 91% savings for interruptible workloads like batch processing. Sustained Use Discounts (SUDs) are automatic credits for running resources a large portion of the month. Managing costs effectively uses tools like Active Assist for recommendations and Cloud Billing reports for tracking. Following a FinOps approach helps teams stay accountable for their cloud budget and maintain long-term financial health.
Supporting the application design
Supporting the application design involves ensuring the cloud infrastructure properly hosts and enables the application's intended functions, performance, and user experience.
Integration patterns with external systems
Integration patterns with external systems define how the cloud solution connects and exchanges data with services outside of Google Cloud, such as on-premises systems or third-party APIs.
Movement of data
Movement of data refers to the strategies and services used to ingest, transfer, and replicate data within and between cloud environments to support processing and analytics.
Design decision trade-offs
Design decision trade-offs involve evaluating the pros and cons of different architectural choices, balancing factors like cost, performance, security, and complexity to meet business requirements.
Workload disposition strategies (e.g., build, buy, modify, or deprecate)
Workload disposition strategies involve deciding whether to build a new solution, buy a commercial product, modify an existing system, or deprecate a legacy workload when moving to the cloud.
Success measurements use key performance indicators (KPIs), return on investment (ROI), and other metrics to evaluate whether the cloud solution is delivering the intended business value and technical performance.
Security and compliance
Security and compliance encompass the practices, controls, and configurations implemented to protect data and systems and ensure adherence to legal and regulatory standards.
Observability
Observability is the capability to understand a system's internal state by collecting and analyzing telemetry data, such as logs, metrics, and traces, to monitor health and troubleshoot issues.
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