2021 article
Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency
49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021), Vol. 53, pp. 594–605.
author keywords: blockchain; smart contracts; distributed database; time series; regression; lstm; gas price prediction
TL;DR:
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.
(via Semantic Scholar)
open access via doi.org (publisher)
Source: Web Of Science
Added: October 24, 2022
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.