Fine-tuning the consistency-latency trade-off in quorum-replicated distributed storage systems
Bibliographic record
Abstract
NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases when the need for scalability outweighs the need for transactions. Several of these systems, notably Amazon's Dynamo and its open-source derivatives, provide quorum-based replication and present the application developer with a choice of multiple client-side "consistency levels" that determine the number of replicas accessed by reads and writes. This setting, in turn, affects both the latency and the consistency observed by the client application. Since using a fixed combination of read and write consistency levels for a given application provides only a limited number of discrete options for tuning the consistency-latency trade-off, we investigate techniques that allow more fine-grained tuning as may be required to support consistency guarantees through service level agreements (SLAs). We consider two such techniques, a novel technique that assigns the consistency level on a peroperation basis by choosing randomly between two options (e.g., weak vs. strong consistency) with a tunable probability, and a known technique that uses weak consistency and injects delays into storage operations artificially. We compare and contrast these two techniques experimentally against each other and against combinations of fixed consistency levels using Apache Cassandra deployed in Amazon's EC2 environment.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".