lprof: a non-intrusive request flow profiler for distributed systems
Bibliographic record
Abstract
Applications implementing cloud services, such as HDFS, Hadoop YARN, Cassandra, and HBase, are mostly built as distributed systems designed to scale. In order to analyze and debug the performance of these systems efficiently, it is essential to understand the performance behavior of system requests, both in aggregate and individually.\nlprof is a profiling tool that automatically reconstructs the execution flow of each request in a distributed application. It infers the execution-flow from logs and binary code and thus does not require any modifications to the application. lprof first statically analyzes an application’s binary code to infer how logs can be parsed so that the dispersed and intertwined log entries can be stitched and associated to specific individual requests.\nWe evaluate lprof on the four widely used distributed services mentioned above. The result shows that lprof ’s precision in request extraction is 90%, and lprof is helpful in diagnosing 65% of the random sampled real-world performance anomalies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".