Spectrum: Speedy and Strictly-Deterministic Smart Contract Transactions for Blockchain Ledgers

Zhihao Chen, Tianji Yang, Yixiao Zheng, Zhao Zhang*, Cheqing Jin, Aoying Zhou

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Today, blockchain ledgers utilize concurrent deterministic execution schemes to scale up. However, ordering fairness is not pre served in these schemes: although they ensure all replicas achieve the same serial order, this order does not always align with the fair, consensus-established order when executing smart contracts with runtime-determined accesses. To preserve ordering fairness, an intuitive method is to concurrently execute transactions and re-execute any order-violating ones. This in turn increases unforeseen conflicts, leading to scaling bottlenecks caused by numerous costly aborts under contention. To address these issues, we pro pose Spectrum, a novel deterministic execution scheme for smart contract execution on blockchain ledgers. Spectrum preserves the consensus-established serial order (so-called strict determinism) with high performance. Specifically, we leverage a speculative deterministic concurrency control to execute transactions in speculation and enforce an agreed-upon serial order by aborting and re-executing any mis-speculated ones. To overcome the scaling bottleneck, we present two key optimizations based on speculative processing: operation-level rollback and predictive scheduling, for reducing both the overhead and the number of mis-speculations. We evaluate Spectrum by executing EVM-based smart contracts on popular benchmarks, showing that it realizes fair smart contract execution by preserving ordering fairness and outperforms competitive schemes in contended workloads by 1.4x to 4.1x.

Original languageEnglish
Pages (from-to)2541-2554
Number of pages14
JournalProceedings of the VLDB Endowment
Volume17
Issue number10
DOIs
StatePublished - 2024
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: 24 Aug 202429 Aug 2024

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