Scalable, near-zero loss disaster recovery for distributed data stores
Bibliographic record
Abstract
This paper presents a new Disaster Recovery (DR) system, called Slogger, that differs from prior works in two principle ways: (i) Slogger enables DR for a linearizable distributed data store, and (ii) Slogger adopts the continuous backup approach that strives to maintain a tiny lag on the backup site relative to the primary site, thereby restricting the data loss window, due to disasters, to milliseconds. These goals pose a significant set of challenges related to consistency of the backup site's state, failures, and scalability. Slogger employs a combination of asynchronous log replication, intra-data center synchronized clocks, pipelining, batching, and a novel watermark service to address these challenges. Furthermore, Slogger is designed to be deployable as an "add-on" module in an existing distributed data store with few modifications to the original code base. Our evaluation, conducted on Slogger extensions to a 32-sharded version of LogCabin, an open source key-value store, shows that Slogger maintains a very small data loss window of 14.2 milliseconds which is near the optimal value in our evaluation setup. Moreover, Slogger reduces the length of the data loss window by 50% compared to incremental snapshotting technique without having any performance penalty on the primary data store. Furthermore, our experiments demonstrate that Slogger achieves our other goals of scalability, fault tolerance, and efficient failover to the backup data store when a disaster is declared at the primary data store.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".