Scaling Permissioned Blockchains via Sharding
Notice bibliographique
Résumé
Traditional distributed systems, such as those used in banking and real estate, require a trusted third party to operate and maintain them, which is highly dependent on the reliability of the operator. Since Bitcoin was introduced by Nakamoto in 2008, blockchain technology has been considered as a promising solution to the trust issue raised by the traditional centralized approach.Blockchain is now used by most cryptocurrencies and has meaningful applications in other areas, such as logistics and supply chain management. However, scalability remains a major limitation. Various techniques are being investigated to tackle the scalability issue. Sharding is an intuitive approach to improve the scalability of blockchain systems. This thesis explores sharding techniques in permissioned blockchains. \nFirst of all, two techniques are examined for interleaving the shards of permissioned blockchains, which are referred to as strong temporal coupling and weak temporal coupling. The analysis and experiment results show that 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. \nSecond, we propose Antipaxos, a leaderless consensus protocol that reaches agreement on multiple proposals with a fast path solution in the failure-free case, and falls back on a slow path to handle other cases. A new agreement problem, termed as k-Interactive Consistency is formalized first. Then, two algorithms to solve this problem are proposed under the crash failure model and Byzantine failure model, respectively. We prove the safety and liveness of the proposed algorithms, and present an experimental evaluation of their performance in the Amazon cloud. Both the crash-tolerant and Byzantine-tolerant designs reach agreement on n batches of proposals with Θ(n2) messages. This leads to the linear complexity of each batch in one consensus cycle, rather than a single batch of proposals per cycle in conventional solutions. The experiments show that our algorithms achieve not only lower execution latency but also higher peak throughput in the failure-free case when deployed in a geo-distributed environment. \nLastly, we introduce a full sharding protocol, Geochain, for permissioned blockchains. The transaction latency is minimized by clustering participants using their geographical properties--locality. In addition, the locality is also being used to decide the transaction placement which suggests 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 Hyperleder 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».