Performance and Fault Tolerance Trade-offs in Sharded Permissioned Blockchains
Bibliographic record
Abstract
Blockchain has become a promising technology in distributed systems in recent years, but scalability remains a major limitation. The traditional approach to scalability, namely sharding, does not solve the problem easily because the process of interleaving blocks stored in different shards to create a unified master ledger is difficult to parallelize effectively. This paper examines two techniques for interleaving the shards of permissioned blockchains, which we refer to as strong temporal coupling and weak temporal coupling. We describe the algorithmic foundations of both techniques, and evaluate their latency-throughput tradeoff experimentally. Strong coupling loses performance when different shards grow unevenly, but outperforms weak coupling in a wide-area environment due to its inherent efficiency. Weak coupling, in contrast, deals naturally with load imbalance across shards and in fact tolerates shard failures without any additional effort, but loses performance when running on a high-latency network due to the additional coordination performed.
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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 | 0.004 | 0.012 |
| 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 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".