Methods and Tools for Ensuring Observability in Distributed Software Systems

Vadym Shevchenko

Citation: Vadym Shevchenko, "Methods and Tools for Ensuring Observability in Distributed Software Systems", Universal Library of Innovative Research and Studies, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article presents a comprehensive analysis of methods and tools for ensuring observability in distributed software systems, including high-performance computing environments, container-based platforms, and microservice architectures. The study is based on comparing structural properties of telemetry, data-collection models, and mechanisms for reconstructing causal relationships as described in contemporary scientific publications. It examines differences in execution-context formation, measurement accuracy, and metric reproducibility across environments with varying workload dynamics. Special attention is given to the impact of architectural constraints on resource attribution, trace-data interpretation, and the stability of analytical outcomes. The practical consequences for engineers are outlined, including the need to standardize context propagation in HPC, develop valid energy-attribution models in Kubernetes, implement consistent tracing mechanisms in microservices, and apply telemetry filtering in causal-analysis workflows. The study demonstrates that the key condition for mature observability is not the volume of collected data but the coherence and reproducibility of measurement pipelines, which enable a holistic understanding of distributed-system behavior. The article may be useful for observability-engineering specialists, distributed-application developers, system architects, and researchers studying telemetry-interpretation methods in complex computational environments.


Keywords: Observability, Distributed Systems, Telemetry, Tracing, High-Performance Computing, Kubernetes, Microservices, Causal Analysis.

Download doi https://doi.org/10.70315/uloap.ulirs.2026.0301006