Combining Distributed and Kernel Tracing for Performance Analysis of Cloud Applications
Why this work is in the frame
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Bibliographic record
Abstract
Distributed tracing allows tracking user requests that span across multiple services and machines in a distributed application. However, typical cloud applications rely on abstraction layers that can hide the root cause of latency happening between processes or in the kernel. Because of its focus on high-level events, existing methodologies in applying distributed tracing can be limited when trying to detect complex contentions and relate them back to the originating requests. Cross-level analyses that include kernel-level events are necessary to debug problems as prevalent as mutex or disk contention, however cross-level analysis and associating events in the kernel and distributed tracing data is complex and can add a lot of overhead. This paper describes a new solution for combining distributed tracing with low-level software tracing in order to find the latency root cause better. We explain how we achieve a hybrid trace collection to capture and synchronize both kernel and distributed request events. Then, we present our design and implementation for a critical path analysis. We show that our analysis describes precisely how each request spends its time and what stands in its critical path while limiting overhead.
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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 | 0.000 |
| 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