Distributed computation of the critical path from execution traces
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
Abstract Due to the ever‐increasing number of computer nodes in distributed systems, efficient and effective tools have become crucial for their analysis. Although several efficient methods have been proposed to monitor and profile distributed systems, tracing remains the most effective solution for in‐depth system analysis. Tracing is the act of collecting a trace, which is a sequence of low‐level events generated by the kernel or the userspace. After data collection, the most important part is the event analysis. The paradigm and choice of graphs determine the ability of the user to detect abnormal behaviors and identify their root cause. Although tracing is a highly effective approach to analyzing complex systems, the scalability of the current analysis tools is limited. As a consequence, tracing is often impractical for large distributed systems. This paper identifies the shortcomings of the current approaches, most notably the critical path computation and the trace file transfer between nodes. Then, this paper proposes new solutions to these drawbacks, most notably a distributed algorithm to compute the critical path, that does not aggregate all traces in a single node, and an efficient architecture to perform tracing on distributed systems. These new solutions are made publically available.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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