GeoChain: A Locality-Based Sharding Protocol for Permissioned Blockchains
Bibliographic record
Abstract
Blockchain is a distributed ledger that uses cryptography and consensus protocols to record a growing list of transactions in a tamper-resistant manner. Scalability is one of the main problems that limit its usage. This paper introduces a full sharding protocol, Geochain, for permissioned blockchains. We first clarify the limitations of state-of-the-art sharding protocols. Then, we propose a locality-based sharding protocol that achieves high scalability. We optimize inter-shard performance by clustering participants using their geographical properties, locality. In addition, the locality is also employed to decide the transaction placement which results in a low ratio of cross-shard transactions for applications, such as everyday banking, retail payments, and electric vehicle charging. We also propose a client-driven efficient mechanism to handle cross-shard transactions and present an analysis. This enables clients to manage their assets across different shards directly. A prototype is implemented on top of Hyperledger Fabric v2.3 and evaluated on Amazon EC2. The experiments show that our protocol doubles the peak throughput, even with a high ratio of cross-shard transactions, while minimizing the transaction latency.
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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 | 0.000 | 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".