High latency cause detection using multilevel dynamic analysis
Bibliographic record
Abstract
The performance of applications remains a major concern to programmers. An unexpected latency can be caused by a bug or a bad program design, but it can also be caused by external factors such as resource contention or system overload. There exist tools, program profilers, that are used to detect latency. These tools, however, provide a limited view of a system's execution. For example, user space profilers can only detect slow functions but are unable to pinpoint the root causes-whether the problem comes from a slow I/O operation, interrupt, lock contention, or other problems. Kernel tracers, on the other hand, are able to collect detailed information about the operating system execution at various levels from hardware counters to system calls, disks, network I/O, etc, from which the main performance problems can be detected. In this paper, we combine user space and kernel space tracing data to understand and diagnose system performance problems and to guide users to identify the root causes. Our approach works by making a single data model by synchronizing and correlating the data gathered from different layers. We show the effectiveness of our approach by applying it to understand the latency of PHP web applications in handling web requests.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".