Automatically generating bursty benchmarks for multitier 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
Burstiness in resource consumption of requests has been recently observed to be a fundamental performance driver for multi-tier applications. This motivates the need for a methodology to create benchmarks with controlled burstiness that helps to improve the effectiveness of system sizing efforts and makes application testing more comprehensive. We tackle this problem using a model-based technique for the automatic and controlled generation of bursty benchmarks. Phase-type models are constructed in an automated manner to model the distribution of service demands placed by user sessions on various system resources. The models are then used to derive session submission policies that result in user-specified levels of service demand burstiness for resources at the different tiers in a system. A case study using a three-tier TPC-W testbed shows that our method is able to control and predict burstiness for session service demands and to cause dramatic latency and throughput degradations that are not visible with the same session mix and no burstiness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| 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 | 0.001 | 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