Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency
Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency
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DOI：
10.1016/j.promfg.2021.06.060
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Hasan;Kemafor Ogan;B. Starly
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文献类型：
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作者：
M. Hasan;Kemafor Ogan;B. Starly
基于区块链的去中心化云制造即服务 (CMaaS) 平台使客户能够通过加密安全网络访问大容量的制造节点。近年来，以太坊网络已成为一种流行的区块链框架，用于在分散式 CMaaS 中提供专有制造数据的来源和可追溯性。然而，以太坊生态系统仅被设计用于存储和传输少量金融交易数据，并且几乎没有采取任何措施使其成为 CMaaS 系统中大型制造数据流的有效存储库。在本文中，作者在之前的工作基础上报告了中间件软件架构的设计、实现和验证，这些架构允许基于以太坊的分布式 CMaaS 平台利用以太坊生态系统的安全资产模型和去中心化 BigchainDB 数据库平台的不可变大数据存储功能的优势。提出了一种由高效通信协议和区块链预言机支持的新型混合区块链架构。该架构允许将大型制造数据流传输和不可变存储到全球 BigchainDB 节点上，从而允许数据丰富的制造交易绕过以太坊生态系统的交易费用。此外，还提出了一种基于机器学习的时间序列推理模型，可以预测未来的以太坊天然气价格。这使得 CMaaS 平台智能合约能够明智地分配 Gas 价格限制，从而节省因转移或创建资产而产生的交易。这项研究的结果表明，设计的混合架构可以通过将大量数据卸载到 BigchainDB 节点上，从而减少大量计算步骤，从而减少以太坊上的交易费用。基于随机森林回归器的时间序列推理模型已被证明在预测以太坊 Gas 价格方面表现出卓越的性能，这使得 CMaaS 能够避免在以太坊生态系统内的高 Gas 价格期间执行交易。
Blockchain based decentralized Cloud Manufacturing-as-a-Service (CMaaS) platforms enable customers to gain access to a large capacity of manufacturing nodes over cryptographically secure networks. In recent times, the Ethereum network has emerged as a popular blockchain framework for providing provenance and traceability of proprietary manufacturing data in decentralized CMaaS. However, the Ethereum ecosystem was only designed to store and transmit low volume financial transaction data and little has been done to make it an efficient repository of large manufacturing data streams in CMaaS systems. In this paper, the authors build on their previous work and report the design, implementation, and validation of middleware software architectures that allow Ethereum based distributed CMaaS platforms to harness the benefits of the secure asset models of the Ethereum ecosystem and the immutable big data storage capabilities of the decentralized BigchainDB database platform. A novel hybrid blockchain architecture enabled by efficient communication protocols and blockchain oracles is proposed. This architecture allows the transfer and immutable storage of large manufacturing data streams onto global BigchainDB nodes allowing data rich manufacturing transactions to bypass the transaction fees of the Ethereum ecosystem. Additionally, a machine learning based time series inference model is proposed which enables the forecast of Ethereum gas price into the future. This allows the CMaaS platform smart contracts to judiciously assign gas price limits and hence save on transactions ensuing from transfer or creation of assets. The outcomes of this research show that the designed hybrid architecture can lead to the reduction of significant number of computational steps and hence transaction fees on Ethereum by offloading large volume data onto BigchainDB nodes. A Random Forest regressor based time series inference model has been shown to exhibit superior performance in the prediction of Ethereum gas price, that allows the CMaaS to avoid executing transactions in periods of high gas prices within the Ethereum ecosystem.