lprof: a non-intrusive request flow profiler for distributed systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 sys-tems effectively and efficiently, it is essential to under-stand the performance behavior of service requests, both in aggregate and individually. lprof is a profiling tool that automatically reconstructs the execution flow of each request in a distributed appli-cation. In contrast to existing approaches that require in-strumentation, lprof infers the request-flow entirely from runtime logs and thus does not require any modifications to source code. lprof first statically analyzes an applica-tion’s binary code to infer how logs can be parsed so that the dispersed and intertwined log entries can be stitched together and associated to specific individual requests. We validate lprof using the four widely used dis-tributed services mentioned above. Our evaluation shows lprof ’s precision in request extraction is 90%, and lprof is helpful in diagnosing 65 % of the sampled real-world performance anomalies. 1
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it