Low-Latency Transaction Scheduling via Userspace Interrupts: Why Wait or Yield When You Can Preempt?
Why this work is in the frame
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Bibliographic record
Abstract
Traditional non-preemptive scheduling can lead to long latency under workloads that mix long-running and short transactions with varying priorities. This occurs because worker threads tend to monopolize CPU cores until they finish processing long-running transactions. Thus, short transactions must wait for the CPU, leading to long latency. As an alternative, cooperative scheduling allows for transaction yielding, but it is difficult to tune for diverse workloads. Although preemption could potentially alleviate this issue, it has seen limited adoption in DBMSs due to the high delivery latency of software interrupts and concerns on wasting useful work induced by read-write lock conflicts in traditional lock-based DBMSs. In this paper, we propose PreemptDB, a new database engine that leverages recent userspace interrupts available in modern CPUs to enable efficient preemptive scheduling. We present an efficient transaction context switching mechanism purely in userspace and scheduling policies that prioritize short, high-priority transactions without significantly affecting long-running queries. Our evaluation demonstrates that PreemptDB significantly reduces end-to-end latency for high-priority transactions compared to non-preemptive FIFO and cooperative scheduling methods.
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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 | 0.001 | 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 | 0.009 | 0.003 |
| 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