Advancing Intelligent Observability Frameworks for Large-Scale Cloud Reliability Engineering
DOI:
https://doi.org/10.15662/1rghna04Keywords:
cloud reliability engineering, observability, distributed systems, anomaly detection, machine learning, SRE, microservices, AIOps, telemetry, MTTDAbstract
The rapid emergence of cloud-native applications and microservices has resulted in an unprecedented complexity of the production environment, which makes traditional monitoring methods ineffective in ensuring high reliability. This paper is a description of an in-depth exploration of Intelligent Observability Frameworks (IOF). These frameworks combine metrics, logs, traces and events into a single telemetry mesh that is augmented with machine learning methods employed to detect anomalies, conduct root cause analysis and predict failures. Based on the analysis of the use cases of these frameworks in production cloud environments, we show that with the adoption of IOFs the Mean Time to Detect (MTTD) can be reduced up to 78% and Mean Time to Resolve (MTTR) – up to 87.5%. We establish a multi-layer architecture, comprising of the layers of data collection, stream processing, advanced AI-driven analytics, and auto-remediation services. Our approach has been validated experimentally on real workload of cloud services over 12 months and 93% F1-scores are achieved by our system on microservice environments.
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