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
Distributed LSM-based databases face throughput and latency issues due to load imbalance across instances and interference from background tasks such as flushing, compaction, and data migration. Hailstorm addresses these problems by deploying the database storage engines over a distributed filesystem that disaggregates storage from processing, enabling storage pooling and compaction offloading. Hailstorm pools storage devices within a rack, allowing each storage engine to fully utilize the aggregate rack storage capacity and bandwidth. Storage pooling successfully handles load imbalance without the need for resharding. Hailstorm offloads compaction tasks to remote nodes, distributing their impact, and improving overall system throughput and response time. We show that Hailstorm achieves load balance in many MongoDB deployments with skewed workloads, improving the average throughput by 60%, while decreasing tail latency by as much as 5X. In workloads with range queries, Hailstorm provides up to 22X throughput improvements. Hailstorm also enables cost savings of 47-56% in OLTP workloads.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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