FLEET: High-Performance Durable Replicated State Machines Using Scattered and Coordinated Log Entries
Bibliographic record
Abstract
Distributed coordination services are fundamental components of distributed systems, employing durable replicated state machines (RSMs) to ensure consistency across replicas and prevent data loss, even in the event of all nodes failing. These services typically rely on persistent logs for rapid recovery, as a universally agreed-upon log allows replicas to restore their state by sequentially replaying ordered log entries. However, the requirement for a totally ordered log inherently limits opportunities for parallelism. This paper introduces Fleet, a high-performance durable RSM protocol that combines a hybrid scattered-entry log with an asynchronous ordered log. Our approach integrates synchronous persistence of scattered entries with asynchronous persistence of ordered entries, ensuring both rapid recovery and high levels of parallelism. Additionally, we propose a parallel applying optimization for the etcd database, named pre-apply. Experimental results demonstrate that Fleet significantly outperforms Raft and Scalog in terms of throughput and latency, achieving up to 10× the throughput under specific configurations and scaling effectively across multiple nodes. Additionally, with the pre-apply optimization, Fleet delivers a 10-fold increase in throughput compared to sequential applying on etcd. Although Fleet incurs a 5% overhead in recovery time during leader failure, this delay is tolerable given the rarity of such events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".