Non-intrusive performance profiling for entire software stacks based on the flow reconstruction principle
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
Understanding the performance behavior of distributed server stacks at scale is non-trivial. The servicing of just a single request can trigger numerous sub-requests across heterogeneous software components; and many similar requests are serviced concurrently and in parallel. When a user experiences poor performance, it is extremely difficult to identify the root cause, as well as the software components and machines that are the culprits.This paper describes Stitch, a non-intrusive tool capable of profiling the performance of an entire distributed software stack solely using the unstructured logs output by heterogeneous software components. Stitch is substantially different from all prior related tools in that it is capable of constructing a system model of an entire software stack without building any domain knowledge into Stitch. Instead, it automatically reconstructs the extensive domain knowledge of the programmers who wrote the code; it does this by relying on the Flow Reconstruction Principle which states that programmers log events such that one can reliably reconstruct the execution flow a posteriori.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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