Optimization of Cloud-Native Observability for Artificial Intelligence Driven Resilient Infrastructure Monitoring and Operational Excellence
DOI:
https://doi.org/10.15680/vf6ejm41Keywords:
Cloud-Native Observability, Artificial Intelligence for IT Operations, OpenTelemetry, eBPF, Dynamic Tail-Sampling, Infrastructure Resilience, Operational ExcellenceAbstract
The orchestration of modern cloud-native infrastructures has grown increasingly complex due to the widespread deployment of microservices, distributed topologies, and elastic container clusters. Traditional monitoring frameworks rely on static thresholds and reactive, siloed tracking of metrics, logs, and traces. These legacy approaches fail to maintain infrastructure resilience or diagnose modern transient anomalies, which often leads to prolonged downtime. This paper presents an optimized, enterprise-ready architectural paradigm for cloud-native observability driven by artificial intelligence. By unifying the OpenTelemetry standardization framework with distributed eBPF (Extended Berkeley Packet Filter) kernel-level telemetric sampling, this system achieves real-time data ingestion across highly distributed architectures. We formalize an intelligent operational layer powered by specialized AI engines. These models ingest high-throughput telemetry streams to execute automated multi-variate anomaly detection, causal dependency mapping, and proactive capacity forecasting. Crucially, the framework addresses data management overhead by introducing dynamic tail-sampling heuristics and adaptive log-cardinality reduction policies, which significantly lower storage and compute costs without sacrificing trace fidelity. Empirical validations across multi-cloud clusters demonstrate substantial reductions in mean time to detection (MTTD) and mean time to resolution (MTTR). This research establishes a reliable blueprint for organizations looking to build self-healing, highly resilient infrastructure monitoring systems that achieve true operational excellence.
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