Automatic detection of performance deviations in the load testing of large scale systems
Bibliographic record
Abstract
Abstract—Load testing is one of the means for evaluating the performance of Large Scale Systems (LSS). At the end of a load test, performance analysts must analyze thousands of performance counters from hundreds of machines under test. These performance counters are measures of run-time system properties such as CPU utilization, Disk I/O, memory consumption, and network traffic. Analysts observe counters to find out if the system is meeting its Service Level Agreements (SLAs). In this paper, we present and evaluate one supervised and three unsupervised approaches to help performance analysts to 1) more effectively compare load tests in order to detect performance deviations which may lead to SLA violations, and 2) to provide them with a smaller and manageable set of important performance counters to assist in root-cause analysis of the detected deviations. Our case study is based on load test data obtained from both a large scale industrial system and an open source benchmark application. The case study shows, that our wrapper-based supervised approach, which uses a search-based technique to find the best subset of performance counters and a logistic regression model for deviation prediction, can provide up to 89 % reduction in the set of performance counters while detecting performance deviations with few false positives (i.e., 95 % average precision). The study also shows that the supervised approach is more stable and effective than the unsupervised approaches but it has more overhead due to its semi-automated training phase. Index Terms—Performance, Signature, Machine Learning. I.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".