Scaling Blockchains: A Comprehensive Survey
Bibliographic record
Abstract
Blockchain (e.g., Bitcoin and Ethereum) has drawn much attention and has been widely-deployed in recent years. However, blockchain scalability is emerging as a challenging issue. This paper outlines the existing solutions to blockchain scalability, which can be classified into two categories: first layer and second layer solutions. First layer solutions propose modifications to the blockchain (i.e., changing the blockchain structure, such as block size) while second layer solutions propose mechanisms that are implemented outside of the blockchain. In particular, we focus on sharding as a promising first layer solution to the scalability issue; the basic idea behind sharding is to divide the blockchain network into multiple committees, each processing a separate set of transactions. More specifically, (a) we propose a taxonomy based on committee formation and intra-committee consensus; and (b) we compare the main existing sharding-based blockchain protocols. We also present a performance-based comparative analysis (i.e., throughput and latency), of the advantages, and disadvantages in existing scalability solutions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".