FlexiShard: a Flexible Sharding Scheme for Blockchain based on a Hybrid Fault Model
Bibliographic record
Abstract
One of the major bottlenecks of traditional Blockchain is its low throughput resulting in poor scalability. One way to increase throughput is to shard the network nodes to form smaller groups (shards). There are a number of sharding schemes in the literature with a common goal: nodes are split into groups to concurrently process different sets of transactions. Parallelism is used to enhance scalability, however with a trade-off in fault-tolerance; i.e., the smaller the shard size is, the better is the performance but higher is the fault probability. Contemporary sharding schemes use variants of Byzantine Fault Tolerance (BFT) protocol as their intra-shard consensus algorithms. BFT gives good performance when shard sizes are kept relatively small and maximum allowable faults is below some threshold. However, all these systems make rigid assumptions about their shard sizes and maximum allowable faults which may not be practical at times. In recent years, there have been more practical hybrid fault models in the literature which are better applicable to Blockchain (e.g., hybrid of Byzantine and alive-but-corrupt (abc) faults where the latter only compromises on safety) and corresponding consensus protocols that offer flexibility in choice of fault types and quorum sizes, e.g., Flexible Byzantine Fault Tolerance (Flexible BFT). In this paper, we present a new sharding scheme, FlexiShard, that uses Flexible BFT as its intra-shard consensus algorithm. FlexiShard leverages the notion of flexible Byzantine quorums and the hybrid fault model introduced in Flexible BFT that comprises of Byzantine and abc faults. Use of Flexible BFT allows flexibility in the choice of fault types and choosing shard sizes based on a range of allowable fault thresholds. Additionally, it allows to form shards that can tolerate more total faults than traditional BFT shards of similar size, and hence can deliver similar performance but with more fault-tolerance. To the best of our knowledge, FlexiShard is the first application of Flexible BFT and the hybrid fault model to Blockchain and its sharding. A theoretical analysis of FlexiShard is presented which demonstrates its flexibility and advantages over traditional sharding schemes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".