QDIME: QoS-Aware Dynamic Binary Instrumentation
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
Software systems with quality of service (QoS), such as database management systems and web servers, are ubiquitous. Such systems must meet strict performance requirements. Instrumentation is a useful technique for the analysis and debugging of QoS systems. Dynamic binary instrumentation (DBI) extracts runtime information to comprehend system's behavior and detect performance bottlenecks. However, existing DBI tools are intrusive; adding unacceptable delay to the program execution. Such delay alters the performance requirements and degrades the overall quality and the user experience of the system. Moreover, the delay may change the system behavior, thus, producing misleading run-time information. This paper presents QDIME, a QoS-aware dynamic binary instrumentation technique that respects system's performance requirements. QDIME takes a user-defined QoS threshold as an input and periodically gathers QoS feedback from the system under analysis to decide its instrumentation budget. We implemented QDIME on top of PIN, a popular DBI framework. We evaluated QDIME with Gzip, MySQL server, Apache HTTP server, and Redis. The experiments show that QDIME respects the user-defined QoS threshold and, thus, improves the performance of the monitored application by manifolds. QDIME is able to provide up to 100% instrumentation coverage with an average of 92% when compared to PIN. Moreover, QDIME reduces the slow-down factor of the instrumented application by 1.41, 5.67, and 10.26 folds for Sys-trace, Call-trace, and Branch-profile respectively. A release of QDIME is available for download at https://github.com/pansy-arafa/qdime.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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