SaaS Observability on the Microsoft Power Platform and its Performance Impacts

Modern Software as a Service (SaaS) platforms, such as the Microsoft Power Platform, simplify maintenance efforts through abstraction of underlying layers such as managing server infrastructure or OS-level configurations. Nevertheless, they still allow custom code extensions to extend and customize the application behavior. Yet, this implies restricted control and limited insights with respect to performance.Often, built-in performance reportings do not provide enough detail for efficient problem root cause analysis. Open standards, such as OpenTelemetry, make it easier to implement a more advanced aligned tracing ecosystem. Yet, while monitoring on traditional, non-cloud systems has been thoroughly studied, collecting telemetry data on SaaS systems faces many more complex challenges such as sandboxing, e.g., limited API access, or collecting end-to-end traces through multiple distributed entry points. Besides platform experience, deep technical knowledge about serialization, transmission, and caching costs is essential to understand the process and assess the performance overhead introduced by collecting telemetry data.In this work, we discuss common challenges regarding SaaS observability and the performance impact that telemetry data collection introduces. For this, we modify Intermediate Language (IL) code, i.e., already compiled code, on the Microsoft Power Platform to collect telemetry data for each method execution. A key finding is that a significant and often unreported portion of performance overhead originates from the SaaS platform itself, a crucial distinction from the overhead introduced by telemetry collection. Finally, we propose a concept for adaptive telemetry collection based on execution plan prediction, which aims to balance observability detail with performance overhead by estimating monitoring costs.