Weighted Sampling of Execution Traces: Capturing More Needles and Less Hay

End-to-end tracing has emerged recently as a valuable tool to improve the dependability of distributed systems, by performing dynamic verification and diagnosing correctness and performance problems. Contrary to logging, end-to-end traces enable coherent sampling of the entire execution of specific requests, and this is exploited by many deployments to reduce the overhead and storage requirements of tracing. This sampling, however, is usually done uniformly at random, which dedicates a large fraction of the sampling budget to common, 'normal' executions, while missing infrequent, but sometimes important, erroneous or anomalous executions. In this paper we define the representative trace sampling problem, and present a new approach, based on clustering of execution graphs, that is able to bias the sampling of requests to maximize the diversity of execution traces stored towards infrequent patterns. In a preliminary, but encouraging work, we show how our approach chooses to persist representative and diverse executions, even when anomalous ones are very infrequent.