Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations
DOI:
https://doi.org/10.15680/IJCTECE.2024.0702006Keywords:
SAP Observability, Autonomous Operations, AIOps (Artificial Intelligence for IT Operations), Predictive Operations, Semantic Gap, Azure Monitor for SAP Solutions (AMS), Unified Semantic Plane, SAP S/4HANA, ELK Stack (Elasticsearch, Logstash, Kibana), Azure Log Analytics, Predict-then-Optimize, Chaos Engineering, Stochastic Telemetry, Cost FunctionAbstract
For the better part of two decades, the administration of SAP environments has been characterized by reactive forensics, a structural fragility that the recent industry shift toward cloud-hosted infrastructure has merely digitized rather than resolved. Current scholarship overwhelmingly favors a “hybrid observability” stack bifurcating infrastructure metrics and application logging yet this architectural compromise creates a fatal semantic gap that precludes true predictive modeling. This study challenges the hybrid consensus by proposing a “Unified Semantic Plane” utilizing Azure Monitor for SAP Solutions (AMS). Unlike studies validated through sterile, academic datasets like SAP-SAM, this research employs rigorous chaos engineering injections on production-grade, stochastic telemetry. Results demonstrate that unifying the telemetry stream reduces anomaly detection latency by over 70% and suppresses false positives by 87%, successfully identifying “silent” failure modes such as semantic locks that traditional threshold-based monitoring ignores. These findings suggest that the industry must abandon the bricolage of the hybrid stack in favor of monolithic data planes, moving from deterministic automation to probabilistic “predict-then-optimize” frameworks, even as the ontological gap between technical signal and business value remains the final barrier to full autonomy.
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