AI-Powered Cloud Observability for Proactive Infrastructure Management
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Abstract
Sustaining cloud-computing infrastructure and services in the face of increasing complexity require comprehensive monitoring and analysis of operating conditions and performance. Observability delivers actionable insights from telemetry data by enabling analysis throughout the entire data life cycle, from data collection and feature engineering through detection, diagnosis, and response. Cloud observability typically focuses on understanding the current state of the infrastructure, but integrating artificial intelligence adds predictive and prescriptive capabilities that inform future resource usage requirements and enable proactive adjustments—behavior that SRE teams are likely to welcome despite initial investment costs.
Proactive observability incorporates a range of proactive features, including predicting unusual usage patterns requiring scaling, identifying maintenance opportunities and their risk impact, forecasting demand from new customers or planned marketing campaigns, assessing risk when implementing changes, guiding resource provisioning for new services, and automating runbook execution when known issues occur. These capabilities can be used to augment the quality of service or control costs, with machine learning models learning from past experiences in human-in-the-loop scenarios.
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