Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems
Bibliographic record
Abstract
Distributed deterministic database systems achieve high transaction throughput for geographically replicated data. Supporting transactions with ACID guarantees requires deterministic databases to order transactions globally to dictate execution order. In a geographically distributed environment, ordering transactions globally can take multiple wide-area network (WAN) round trips of messaging, which adds significant latency to transaction response times, leading to poor user experiences. To improve the response time of transactions in deterministic databases, we propose an ordering protocol that can include a transaction in the global order in a single WAN round trip to the primary regions of the data items within the transaction's read and write set. The protocol reduces the cost of determining the global order for all transactions by leveraging deterministic merging of partial sequences of transactions per geographic region. We implement the protocol in Caerus, our geo-replicated deterministic database system that serializably commits and replicates transactions after a delay of only a single WAN round trip of messaging. Using popular workload benchmarks over geographically replicated data in Azure, we show that Caerus outperforms state-of-the-art comparison systems to deliver low-latency transaction execution.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".