The Blockchain has been given great attention in recent literature among emerging technologies in software architectures. More specifically, when verifiable transactions between untrusted parties are concerned in a safe and reliable environment, its peculiar decentralized and tamper-proof structure makes it suitable for a vast class of business domains, such as Cloud Manufacturing, which is a new paradigm in the industry based on cloud technologies. However, the stiffness of existing solutions, that are unable to provide and implement heterogeneous services in a Cloud environment, emphasizes the need of a standard framework to overcome this limit and improve collaboration. Firstly, this paper introduces a Blockchain based platform designed with Smart Contracts for improving digital processes in a manufacturing environment. The primary contribution is the integration of two popular cloud technologies within the Blockchain: Docker, a scalable platform to run applications in lightweight environments, and Cloud Storage. Each process available in the platform requires input files and produces output files by using cloud storage as a repository and it is delivered by the owner as a self-contained Docker image, whose digest is safely stored in the chain. Secondly, with the purpose of selecting the fastest node for each new process instance required by consumers, we introduce a task assignment problem based on a deep learning approach and past metrics. The proposed platform is applied to a real-world industrial case study regarding ophthalmic lenses manufacturing and the optimization of lens surface calculation.

An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing / Volpe, Gaetano; Mangini, Agostino Marcello; Fanti, Maria Pia. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 10:(2022), pp. 79141-79151. [10.1109/ACCESS.2022.3194264]

An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing

Volpe, Gaetano;Mangini, Agostino Marcello;Fanti, Maria Pia
2022-01-01

Abstract

The Blockchain has been given great attention in recent literature among emerging technologies in software architectures. More specifically, when verifiable transactions between untrusted parties are concerned in a safe and reliable environment, its peculiar decentralized and tamper-proof structure makes it suitable for a vast class of business domains, such as Cloud Manufacturing, which is a new paradigm in the industry based on cloud technologies. However, the stiffness of existing solutions, that are unable to provide and implement heterogeneous services in a Cloud environment, emphasizes the need of a standard framework to overcome this limit and improve collaboration. Firstly, this paper introduces a Blockchain based platform designed with Smart Contracts for improving digital processes in a manufacturing environment. The primary contribution is the integration of two popular cloud technologies within the Blockchain: Docker, a scalable platform to run applications in lightweight environments, and Cloud Storage. Each process available in the platform requires input files and produces output files by using cloud storage as a repository and it is delivered by the owner as a self-contained Docker image, whose digest is safely stored in the chain. Secondly, with the purpose of selecting the fastest node for each new process instance required by consumers, we introduce a task assignment problem based on a deep learning approach and past metrics. The proposed platform is applied to a real-world industrial case study regarding ophthalmic lenses manufacturing and the optimization of lens surface calculation.
2022
An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing / Volpe, Gaetano; Mangini, Agostino Marcello; Fanti, Maria Pia. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 10:(2022), pp. 79141-79151. [10.1109/ACCESS.2022.3194264]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/243741
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