Open Source Summit Europe 2024

In the realm of distributed systems and microservices architecture,dynamic API tracing systems play a pivotal role in diagnosing API failures and performance issues.However, current implementations often sample only a fraction of traces(1-5%) due to storage and scale constraints,leading to a skewed distribution favoring normal execution traces.This bias hampers effective diagnosis,impacting both developers and SRE teams.Our proposed solution employs a Machine Learning and Cognitive approach to rectify this imbalance in trace collection. By dynamically adapting based on actual data, the system learns to capture higher-interest traces, helping root causing issues. This innovative approach has significantly improved the Mean Time to Resolve (MTTR) for SRE teams, revolutionizing issue triaging within the organization. The Adaptive Sampling technique, driven by data analysis, standardizes trace distribution, reduces storage requirements, and dynamically adjusts the sampling rate.It lowers costs & resource utilization for tracing services, also streamlines trace metric analysis for SRE,offering a forward-looking solution to common challenges in the Observability and Infrastructure space.