TELEMETRY INTELLIGENCE FOR SAP CLOUD ALM: AN OPENTELEMETRY-BASED FRAMEWORK FOR CORRELATED, LLM-AUGMENTED ROOT CAUSE ANALYSIS

In SAP landscapes, the indirect operational costs of delayed or incorrect root-cause analysis include increased mean time to resolution, increased risk of SLA breaches and overburdening expert analysts who have to connect the dots on multiple monitoring tools and correlate them before formulating a hypothesis. SAP Cloud ALM offers solutions on the data collection end of the problem with Health Monitoring, Integration and Exception Monitoring, status events and a documented API layer. The issue is not a lack of data; rather, evidence from four different monitoring applications must be synthesized manually before it can support action. This paper proposes a telemetry intelligence framework bounded to the four SAP Cloud ALM evidence domains, drawing on the Analytics API, Raw Metrics API, and Raw Logs API as extraction sources. OpenTelemetry normalizes diverse signals into a unified metrics, logs, and events schema. A multi-dimensional correlation engine then correlates related signals by time window, managed component, service, endpoint and message flow. With this correlated evidence, under strict evidence constraints, a large language model provides the root-cause textual narrative, the confidence level and the recommended action for each claim, together with the corresponding telemetry evidence on which the claim is based. The health of the extraction pipeline is treated as an additional reliability-control layer: the confidence score for each LLM explanation is lowered if the extraction pipeline's health is poor. The contribution is completed by a six-class predictive operations action model and telemetry intelligence maturity model, consisting of five levels. Preliminary validation was conducted in a real SAP Cloud ALM tenant using Health Monitoring and Integration and Exception Monitoring fields across more than five cases. This paper does not include a formal large-scale evaluation or a production-grade scoring implementation for BPM or RUM, which are addressed in companion papers.