Cloud infrastructure monitoring collects and analyzes data from cloud services, providing raw insights into their health. Site Reliability Engineering (SRE) then uses software engineering practices to act on this monitoring data, building automated systems and processes to improve the reliability, scalability, and performance of cloud-native environments. 

How cloud infrastructure monitoring empowers SRE

1. Provides real-time visibility through key metrics
Cloud monitoring tools collect a continuous stream of metrics that SRE teams use to understand the health and performance of their systems. 

• Golden Signals: SRE teams often focus on the four key metrics of latency, traffic, errors, and saturation to get a comprehensive view of service health.
• Detailed Metrics: In addition to golden signals, monitoring tracks lower-level metrics such as CPU and memory usage, disk I/O, and network traffic.
• Predictive Analytics: Modern monitoring tools use AI and machine learning to analyze historical performance data and predict potential issues, enabling SRE teams to take proactive measures. 

2. Enables proactive issue detection and alerts
Effective monitoring includes an alerting system that notifies SREs of potential problems before they impact users. 

• Anomaly Detection: Monitoring platforms use AI to establish baselines of "normal" behavior and automatically alert on unusual patterns, catching issues that might otherwise be missed.
• Incident Triggering: Alerts are configured based on predefined thresholds for critical metrics. When a metric breaches a threshold, an automated alert is sent to the appropriate on-call SRE.
• Intelligent Alerting: SRE practices focus on creating smart, actionable alerts to avoid "alert fatigue" and help the team focus on critical, user-impacting issues. 

3. Drives reliability through data-driven decisions
SRE relies on objective data from monitoring to set and meet reliability targets, rather than relying on gut feelings. 

• Service Level Indicators (SLIs): Monitoring provides the data to define quantitative metrics, such as a service's request latency or error rate.
• Service Level Objectives (SLOs): SREs use SLIs to set a target for a service's reliability (e.g., 99.9% uptime). Monitoring tracks the progress toward these objectives.
• Error Budgets: The SRE team defines an acceptable tolerance for failures based on SLOs. Monitoring data depletes the error budget, and if the budget is spent, the team prioritizes reliability fixes over new features. 

4. Automates operational tasks and incident response
SRE emphasizes using automation to reduce manual, repetitive work (known as "toil"). Monitoring data is the trigger for this automation. 

• Automated Remediation: Simple issues, like restarting a failed server or container, can be automatically resolved based on monitoring alerts, reducing Mean Time to Recovery (MTTR).
• Continuous Integration/Continuous Deployment (CI/CD): SREs build automated pipelines that use monitoring data and SLOs to validate new code. A change that fails to meet reliability targets can be automatically rolled back.
• Runbook Automation: For more complex incidents, SRE teams build automated runbooks that provide a guided, step-by-step response, triggered by specific monitoring alerts. 

Key tools used for cloud monitoring and SRE

• Built-in Cloud Tools: Major cloud providers offer integrated monitoring solutions:
  • AWS: CloudWatch
  • Azure: Azure Monitor
  • Google Cloud: Operations Suite (formerly Stackdriver)
• Third-Party Observability Platforms: These tools often provide a unified view across multi-cloud and hybrid environments.
• Datadog
• New Relic
• Splunk
• Dynatrace