Determining Optimal Shard Size in a Hierarchical Blockchain Architecture
Bibliographic record
Abstract
Monolithic blockchain architectures employed in Bitcoin and other major alt-coins are inherently non-scalable. In recent past, some hierarchical approaches have been explored to shard the decentralized blockchain to improve scalability. However, there is no discussion in the literature about how to determine an optimal shard size to maximize performance and how the presence of malicious or faulty nodes can impact on choosing an optimal shard size. To address these issues, this paper presents a sharding scheme and validation protocols for a hierarchical blockchain architecture named OptiShard. The hierarchy divides the network nodes into multiple disjoint shards and the majority of transactions are distributed among these shards in non-overlapped fashion. Optimal shard size is determined based on two parameters: performance and correctness of transaction validation in the presence of malicious or faulty nodes. OptiShard provides guaranteed majority of good shards, subject to a maximum allowable threshold of faulty nodes, by choosing the right shard size. It also provides a mechanism for identifying faulty shards and discarding all their transactions through the overlapping of a small fraction of transactions across all the shards. Experimental results performed on up to 800 Amazon EC2 nodes conform to the theoretical performance analyses and exhibit the impact of sharding the network on performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".