Q-Cop: Avoiding bad query mixes to minimize client timeouts under heavy loads
Bibliographic record
Abstract
In three-tiered web applications, some form of admission control is required to ensure that throughput and response times are not significantly harmed during periods of heavy load. We propose Q-Cop, a prototype system for improving admission control decisions that considers a combination of the load on the system, the number of simultaneous queries being executed, the actual mix of queries being executed, and the expected time a user may wait for a reply before they or their browser give up (i.e., time out). Using TPC-W queries, we show that the response times of different types of queries can vary significantly depending not just on the number of queries being processed but on the mix of other queries that are running simultaneously. We develop a model of expected query execution times that accounts for the mix of queries being executed and integrate this model into a three-tiered system to make admission control decisions. Our results show that this approach makes more informed decisions about which queries to reject and as a result significantly reduces the number of requests that time out. Across the range of workloads examined an average of 47% fewer requests are unsuccessful than the next best approach.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".