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 problem. 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 introduces overhead. 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 implement these techniques in a prototype system with a Bitcoin-like transaction structure, using the EPaxos consensus protocol for transaction ordering. Our experimental results show that strong coupling can achieve lower latency as compared to weak coupling but same level of peak throughput. However, strong coupling requires all shards to grow at the same rate, and cannot tolerate any shard failure. In contrast, the higher latency of weak coupling is because of the consensus strategy it uses to order the blocks. However, if shard failure occurs, weak coupling can still make progress without stalling the whole system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".