RemusDB
Bibliographic record
Abstract
In this paper we present a technique for building a high-availability (HA) database management system (DBMS). The proposed technique can be applied to any DBMS with little or no customization, and with reasonable performance overhead. Our approach is based on Remus, a commodity HA solution implemented in the virtualization layer, that uses asynchronous virtual machine (VM) state replication to provide transparent HA and failover capabilities. We show that while Remus and similar systems can protect a DBMS, database workloads incur a performance overhead of up to 32% as compared to an unprotected DBMS. We identify the sources of this overhead and develop optimizations that mitigate the problems. We present an experimental evaluation using two popular database systems and industry standard benchmarks showing that for certain workloads, our optimized approach provides very fast failover (≤ 3 seconds of downtime) with low performance overhead when compared to an unprotected DBMS. Our approach provides a practical means for existing, deployed database systems to be made more reliable with a minimum of risk, cost, and effort. Furthermore, this paper invites new discussion about whether the complexity of HA is best implemented within the DBMS, or as a service by the infrastructure below it.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.035 |
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".