CloudMonitoring

Cloudmonitoring refers to the collection, aggregation, analysis, and visualization of telemetry from cloud-based applications, services, and infrastructure. It aims to ensure performance, reliability, security, and cost efficiency across public, private, and hybrid clouds.

It covers infrastructure monitoring of virtual machines, containers, serverless components, and networks; application performance monitoring to track user-facing latency and error rates; log analytics; and service health across distributed systems. Telemetry is typically gathered as metrics, logs, traces, and events. Common metrics include latency, request rates, error rates, CPU and memory utilization, disk I/O, network throughput, and service uptime. SLOs and SLIs are used to translate technical performance into business targets.

Architecture typically involves data collection via agents, exporters, and cloud-native APIs, pipelines to time-series databases, processing and alerting engines, and dashboards for visualization. Alerts are often integrated with incident management workflows. Data retention, normalization, and labeling are important for cross-service correlation.

Tools range from cloud-native offerings such as AWS CloudWatch, Azure Monitor, and Google Cloud Operations Suite to open-source stacks with Prometheus and Grafana, and commercial platforms such as Datadog and New Relic. Best practices include instrumenting code with consistent metrics, using multi-dimensional labeling, setting meaningful SLOs, correlating metrics with traces and logs, avoiding alert fatigue through alert rules, and managing cost through sampling and retention policies.

Challenges include multi-cloud complexity, high data volumes, cost control, privacy and compliance, agent management, and securing telemetry data. The field is evolving toward enhanced observability, AI-assisted anomaly detection, and tighter integration with incident response and automated remediation.