Classification of Post-deployment Performance Diagnostic Techniques for Large-scale Software Systems
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
Today's large-scale software systems (LSSs) such as Facebook, Google, Amazon and many other contemporary datacenters comprise hundreds or thousands of machines running complex applications that require high availability and responsiveness. These LSSs must be carefully monitored for performance bottlenecks before a serious harm is done. Performance analysts have to deal with the tedious task of monitoring the performance of these LSSs to avoid any service level agreements (SLA) violations and to ensure their failure free operations. There do exist several post-deployment performance diagnostic (PPD) techniques for to help analysts diagnose performance problems in the field, i.e., after the software is deployed. However, there is no classification of the proposed PPD techniques to understand their objectives and characteristics. In this paper, we classify the existing PPD techniques along multiple categories. The classification of PPD techniques will provide a guideline for performance analysts and practitioners of LSS to choose techniques suitable for their need. Moreover, the classification will also help researcher understand and fill gaps, i.e., dedicate their research efforts to categories that have received little attention in the past.
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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 | 0.002 | 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