On Scaling Decentralized Blockchains - (A Position Paper)

By Kyle Croman, Christian Decker, Ittay Eyal, Adem Efe Gencer, Ari Juels, Ahmed E. Kosba, Andrew K. Miller, Prateek Saxena, Elaine Shi, Emin Gün Sirer and 2 more. Financial Cryptography Workshops, 2016

540 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/75d83792b880757a09e9a72978cc29beb57c4ad5

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding (2018, background): “Sharding could benefit DLs [15] by reducing the transaction processing load on each validator and by increasing the system’s total processing capacity proportionally with the number of participants.”
Instantaneous Decentralized Poker (2017, background): “…micropayment channels can reduce the amount of transaction data that the decentralized cryptocurrency network maintains, and thus the long-term scalability pressures that a cryptocurrency faces can be relieved by well-functioning oﬀ-chain payment channels (see, e.g., [17] for further discussion).”
Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey (2020, unknown): “Croman et al. (2016) mainly improved the scalability of blockchain on latency, throughput and other parameters.”
Performance Analysis of Private Blockchain Platforms in Varying Workloads (2017, background): “A more recent study in [5] focuses more blockchain’s throughput and latency where the adjustment of block sizes and intervals are proposed as a first step.”
From blockchain consensus back to Byzantine consensus (2017, background): “Some solutions like PBFT [13] were proposed, however, there are known not to scale to a number of participants as large as mainstream public blockchain systems [16, 44].”
The Bitcoin Backbone Protocol: Analysis and Applications (2015, unknown): “…(i.e., reducing the wait time parameter u in the Liveness property) is also an interesting question with implications to practice (since real-world payment systems benefit greatly from fast transaction confirmation and verification—see, for example, [23] for scalability issues and suggestions).”
SoK: Consensus in the Age of Blockchains (2017, background): “ency: different nodes might end up having different views of the blockchain leading to forks. Additionally, Bitcoin suffers from poor performance which cannot be remedied without fundamental redesign [49] and its PoW consumes a huge amount of energy [131]. This has led to a plethora of proposals for”
BlocHIE: A BLOCkchain-Based Platform for Healthcare Information Exchange (2018, background): “There are a lot of research on setting the optimal block size [22][23].”
Scalable Bias-Resistant Distributed Randomness (2017, background): “Anytrustbased systems, such as Herbivore [32], Dissent [60], and Vuvuzela [59], as well as sharded blockchains [23], use biasresistant public randomness for scalability by sharding participants into smaller groups.”
Performance Modeling of PBFT Consensus Process for Permissioned Blockchain Network (Hyperledger Fabric) (2017, background): “Although crypto-currency is an interesting application, its performance is worrisome, with time to confirmation of transaction of ten minutes or more, achieving a maximum throughput of 7 transactions per second [4].”
Erays: Reverse Engineering Ethereum's Opaque Smart Contracts (2018, background): “, gambling or trading) [34] and measuring the performance of the underlying peer-to-peer network [19, 20, 36, 37].”
Hybrid Consensus: Efficient Consensus in the Permissionless Model (2016, background): “[22] point out, the Bitcoin network can sustain at most 7 tx/sec, at a transaction confirmation time of 10+ min (c.”
Enhancing Bitcoin Security and Performance with Strong Consistency via Collective Signing (2016, background): “Besides the issues outlined above, there are many more interesting open questions worth considering: Sharding [17] presents a promising approach to scale distributed protocols and was already studied for private blockchains [18].”