Traces Synchronization in Distributed Networks
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
This article proposes a novel approach to synchronize a posteriori the detailed execution traces from several networked computers. It can be used to debug and investigate complex performance problems in systems where several computers exchange information. When the distributed system is under study, detailed execution traces are generated locally on each system using an efficient and accurate system level tracer, LTTng. When the tracing is finished, the individual traces are collected and analysed together. The messaging events in all the traces are then identified and correlated in order to estimate the time offset over time between each node. The time offset computation imprecision, associated with asymmetric network delays and operating system latency in message sending and receiving, is amortized over a large time interval through a linear least square fit over several messages covering a large time span. The resulting accuracy is such that it is possible to estimate the clock offsets in a distributed system, even with a relatively low volume of messages exchanged, to within the order of a microsecond while having a very low impact on the system execution, which is sufficient to properly order the events traced on the individual computers in the distributed system.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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