MLOLET - Machine Learning Optimized Load and Endurance Testing: An industrial experience report
Bibliographic record
Abstract
Load testing is essential for ensuring the performance and stability of modern large-scale systems, which must handle vast numbers of concurrent requests. Traditional load tests, often requiring extensive execution times, are costly and impractical within the short release cycles typical of contemporary software development. In this paper, we present our experience deploying MLOLET, a machine learning optimized load testing framework, at Ericsson. MLOLET addresses key challenges in load testing by determining early stop points for tests and forecasting throughput and response time trends in production environments. By training a time-series model on key performance indicators (KPIs) collected from load tests, MLOLET enables early detection of abnormal system behavior and provides accurate performance forecasting. This capability allows load test engineers to make informed decisions on resource allocation, enhancing both testing efficiency and system reliability. We document the design of MLOLET, its application in industrial settings, and the feedback received from its implementation, highlighting its impact on improving load testing processes and operational performance.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".