Wait Analysis of Distributed Systems Using Kernel Tracing
Bibliographic record
Abstract
We propose a new class of profiler for distributed and heterogeneous systems. In these systems, a task may wait for the result of another task, either locally or remotely. Such wait dependencies are invisible to instruction profilers. We propose a host-based, precise method to recover recursively wait causes across machines, using blocking as the fundamental mechanism to detect changes in the control flow. It relies solely on operating system events, namely scheduling, interrupts and network events. It is therefore capable of observing kernel threads interactions and achieves user-space runtime independence. Given a task, the algorithm computes its active path from the trace, which is presented in an interactive viewer for inspection. We validated our new method with workloads representing major architecture and operating conditions found in distributed programs. We then used our method to analyze the execution behavior of five different distributed systems. We found that the worst case tracing overhead for a distributed application is 18 percent and that the typical average overhead is about 5 percent. The analysis implementation has linear runtime according to the trace size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".