Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study

Pervasive and Mobile Computing xxx (xxxx) xxx

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Pervasive and Mobile Computing journal homepage: www.elsevier.com/locate/pmc

Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study ∗

Xiaobao Zhu a , Jing Shi a , , Samuel Huang a , Bin Zhang b a

Department of Mechanical & Materials Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH 45221, USA b Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA

article

info

Article history: Received 9 November 2018 Received in revised form 14 November 2019 Accepted 23 November 2019 Available online xxxx Keywords: Cloud manufacturing Blockchain technology KNN Ethereum POA Consensus-oriented

a b s t r a c t In the era of cloud computing and Industry 4.0, significant research efforts on cloud manufacturing have been witnessed in recent years. Nevertheless, challenges, such as issues of trust, safety, payment, remain in this emerging area, which cause less confidence for industry to adopt cloud manufacturing. In this regard, the recent development of blockchain technology provides a potential viable solution thanks to its unique advantages in decentralization and security. As such, we propose a new framework of cloud manufacturing by integrating the blockchain technology. In essence, consensus-oriented mechanisms are employed to generate the operating standards for the blockchain cloud manufacturing model. Moreover, based on the open source Ethereum code, we construct a simulation case study for 3D printing services using the proposed framework. A consortium or federated blockchain is simulated which uses Proof-of-Authority (PoA) as the consensus algorithm of block generation. The simulation involves 939 job requests from 100 users, as well as 10 service providers. The k-nearest neighbors (KNN) algorithm is employed to recommend the service provider for each request. The results show that the provider’s score of service evaluation tends to be stabilize, and 934 requests for service are successfully fulfilled by the appropriate providers while the remaining 5 requests fail to be serviced. © 2020 Elsevier B.V. All rights reserved.

1. Introduction Despite a brief dip in 2009 due to the Great Recession, the world’s added value of manufacturing activities overall maintained a solid growth from 8.56 to 11.846 trillion USD between 2000 to 2017 [1]. The increase of manufacturing revenue is attributable to many enabling technologies/concepts such as agile manufacturing [2], virtual manufacturing [3], mass customization, application service provider [4], Internet of Things (IoT), collaborative manufacturing [5], industrial big data [6], cyber–physical system (CPS) [7]. In particular, the concept of cloud manufacturing, inspired by the success of cloud computing and recently proposed as an effective means for treating manufacturing as a service (MaaS), has attracted attention from academia, industry, and governments. In the pioneering work of Li et al. [8], cloud manufacturing is proposed as a new network-based model that follows user requirements, and employs the network and cloud resource platform to organize manufacturing resources to achieve the desired manufacturing services. It is suggested that by integrating cloud computing and other technologies such as Internet ∗ Corresponding author. E-mail address: [email protected] (J. Shi). https://doi.org/10.1016/j.pmcj.2020.101113 1574-1192/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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of Things and artificial intelligence (AI), service systems of cloud manufacturing can be constructed. Cloud manufacturing adopts the service-oriented architecture (SOA) inherited from cloud computing. SOA treats everything as a service, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Other research efforts can also be found in literature. Tao et al. [9] propose a new computing- and service-oriented manufacturing model which consists of four common cloud manufacturing service platforms (i.e., public, private, community, and hybrid service platforms). Also, Tao et al. [10] develop a novel parallel intelligent algorithm to achieve the optimal selection of service composition. Wu et al. [11] indicate that the potential impacts of cloud manufacturing include engineering design, manufacturing, and market and service, and the future work should address the issues of automation and control, business model, information and resource sharing, distributed system simulation, and cost estimation. Qanbari & Dustdar [12] propose a manufacturing mechanism which combines the policies, plans, and templates of the Oasis Topology and Orchestration Specification for Cloud Applications (TOSCA). This mechanism deals with the provisioning, portability, and management of all types of manufacturing resources. A product bill of manufacturing (BOM) is regarded as a bill of manufacturing services (BOMS). The idea enables the service-oriented cloud manufacturing to achieve better feasibility. Helo et al. [13] propose a cloud-based new manufacturing execution systems to integrate information exchange between companies for distributed manufacturing. This new system overcomes the challenges of the management of distributed manufacturing in a multi-company supply chain and processes. Wu et al. [14] propose a new model for the cloud-based design manufacturing (CBDM) and define a systematic requirement checklist. In addition, a smart delivery drone as an idealized CBDM example is developed in the research. Valilai & Houshmand [15] discuss a cloud manufacturing-based solution for additive manufacturing systems. Integrated manufacturing operations and enabled collaboration in distributed networks are the two primary requirements for the globalized perspective of additive manufacturing paradigm. In turn, additive manufacturing allows cloud manufacturing to improve the flexibility of the globalized perspective. Based on quality of service (QoS) evaluation along with the geoperspective, Lartigau et al. [16] propose a method to analyze the transportation impact of the correlation between one cloud service and another. The study can serve as a foundation of recommended algorithm to improve manufacturing automation. What is more, Ahn et al. [17] propose a dynamic enterprise network composition algorithm for solving the enterprise network composition problem for cloud manufacturing, and conduct a simulation study. Xie et al. [18] use a semantic model to represent information resource service which uses semantic links instead of ontologies. Applying the model to cloud manufacturing allows resource service cloud to be integrated automatically and be updated in a distributed manner. Even with the intensive research on cloud manufacturing, some key challenges remain and they significantly limit the practical applications of cloud manufacturing in industry. Tao et al. [9] point out the major issues of cloud manufacturing, namely, the lack of international standards, safety, and security. Rahman et al. [19] list the gaps between the current cloud manufacturing models and the ideal cloud manufacturing environments, and indicate that the current cloud manufacturing models are not effective in defining cost models for ‘‘everything as a service’’, integrating real-time monitoring approaches, integrating all supply chain components, and securing information. Again, many challenges are the result of lack of trust and security mechanism. Indeed, the root issues of trust and security prohibit the implementation of cloud manufacturing systems in industry. The trust issue is comprised of trusts of people to people, people to machine, and machine to machine. The security issue indicates that hack attacks, malicious deceptions, and tampering with data should be mitigated for the services on the cloud manufacturing platform. In addition, an effective payment mechanism which suits for global cloud manufacturing is another requirement. The payment issue is reflected by various currency systems around the world, and very often currency exchange is heavily regulated. To overcome the challenges, limited attempts have been made to discuss the applications of blockchain technology to cloud manufacturing. Hardjono and Smith [20] propose a privacy-preserving method to commission an IoT device into a cloud ecosystem. By using a blockchain, the devices can be trusted without reliance on a trusted third party. The research introduces an architecture which supports anonymous device commissioning and device-owners being remunerated for selling their sensing data to service providers. This architecture also incentivizes device-owners and service providers to share sensor-data in a privacy-preserving manner. Li et al. [21] propose a distributed peer-to-peer service-oriented network architecture for improving the security and scalability of a cloud manufacturing system. The five-layer peerto-peer network architecture is fully decentralized, by adopting the blockchain technology. In addition, Bahga et al. [22] propose a decentralized peer-to-peer platform for industrial internet of things (IIoT). The proposed platform employs blockchain technology to enable the decentralized nodes to cooperate with each other without a trusted third party. In this research, we propose and realize a novel system by integrating cloud manufacturing with blockchain technology, such that the system owns both centralization and decentralization features. This key distinguishing feature is made possible by adopting a consortium blockchain instead of a public or private blockchain. What further distinguishes our work from the limited existing efforts is reflected by a comprehensive design of schemes on order initiation, service, payment, and arbitration in the proposed system. Furthermore, the proposed system is applied to a scenario of cloud 3D printing service, and a simulation study is designed to verify the feasibility. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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2. Blockchain based cloud manufacturing (BBCM) 2.1. Blockchain technology Blockchain technology is commonly recognized as a distributed ledge technology. A blockchain is a database which is also known as the ‘distributed ledger’. Blockchain provides proof of who owns what at any given time, and it is publicly available. Essentially, blockchain technology is a confidence mechanism which is constituted by distributed technologies and consensus mechanisms. The open property of blockchain allows all who are involved to verify the accuracy of trading records. This mechanism can prevent malicious distorted information. A typical application of blockchain is the virtual currency system, in which Bitcoin and Ethereum are the most popular virtual currencies. Recently, a significant number of applications which are based on the blockchain are emerging. Many are developed for financial applications [23], while others include the applications for digital content storage and distribution [24], personal data management [25], supply chain management [26], and smart city [27]. The basic blockchain terminologies are briefly described as follows. Miners are the owners of the proposed system who have permission to create new block through the consensus algorithm, and they are usually the credible industry leaders distributed globally. Nodes are the participants who are approved by the miners through the smart contracts of voting. Node information is recorded into the smart contracts. After being approved by the smart contracts, the idle computing power of nodes can join the miners’ grid computing to obtain gas rewards [28] and block rewards. In a blockchain system, gas fee is the internal pricing for executing a transaction or a contract, while the block reward refers to the new cryptocurrency awarded by the blockchain system to eligible cryptocurrency miners for each block they mine successfully. In a typical blockchain system, miners take both gas rewards and block rewards when they mine a block successfully. Typically, the nodes of the federated blockchain consist of manufacturing enterprises, demand enterprises, equipment maintaining companies, logistic companies, material supply enterprises, research organizations, cryptocurrency exchanges, and so on. In a blockchain system, all the trading records are saved in nodes. Miners are subject to the same rules when they verify and package a new block. This rule is not based on trust but based on a cryptographic algorithm. In the meantime, each transaction needs to be approved by other users in the network. Meanwhile, users are self-disciplined by a consensus algorithm. Any activity of deliberate deception will be rejected and restrained by other nodes. This mechanism allows the system not to need an endorsement authority. Also, a one-way hash algorithm is employed for tamper resistance. Each new block is created by a strict timeline sequence. The property of time irreversibility enables any activity of invading for tampering historic data of blockchain to be retrospected easily. According to Sudhir [29], there are generally three types of blockchains, namely, public blockchain, private blockchain, and consortium (or federated) blockchain (CoFBc). A public blockchain allows any people to participate in reading, writing and auditing the blockchain. Public blockchains are fully distributed and decentralized. The drawbacks include inefficiency, uncontrollability, the weak resistivity to major attacks in situation of limited computing power. On the other hand, in a private blockchain the permission of writing blockchain only belongs one person or organization. In a sense, private blockchains lose the critical feature of decentralization. Lastly, federated blockchains allow more than one entity in charge. In such a blockchain, a group of organizations or representative individuals jointly make decisions for the best benefit of the whole network. With limited number of uses, a federated blockchain is considered partly-decentralized or multicentered. This situation leads to limited computing power. If the proof-of-work (POW) consensus algorithm is applied, the limited total computing power brings about morality risks for federated members. However, the implementation of security protocols such as the practical Byzantine fault tolerance protocol (PBFT) can mitigate this risk. In a blockchain system, the blocks record all of the information of transaction to ensure the reliability of transactions and safety of information. A transaction reflects the transfer of cryptocurrency value that is broadcast to the blockchain network and collected into blocks. A smart contract is a computer code running on the blockchain. This code contains a set of rules under which the stakeholders of the smart contract agree to interact with each other. Smart contracts are self-verifying, self-executing and tamper resistant. Smart contracts can be used to record the information of users, providers and all cloud manufacturing related activities. Smart contracts also can be used to submit a transaction or serve as the voting mechanism to reach arbitration committee decisions, approve industrial standards, and agree to add new members. 2.2. Framework of blockchain based cloud manufacturing The proposed system of blockchain based cloud manufacturing reflects a new model of consensus-oriented manufacturing by combining the blockchain technology with the existing cloud manufacturing concept. Blockchain related techniques such as virtual currency, smart contract, consensus algorithm, and HASH256 encrypted technology are adopted to realize automatic consensus-driven services. The services allow global companies and organizations to possess a variety of safe and reliable, credible, high quality, cheap, easy payable and on-demand manufacturing resources. Fig. 1 shows that the proposed system consists of four components, namely, users, solution computing, service providers, and delivery. In the center, the four core platforms of blockchain, knowledge, consensus standardization protocol, and the Internet are used to guarantee the safety and feasibility of the system. The green arrows show the interaction, communication, and/or Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 1. Operation model of the proposed blockchain based cloud manufacturing system.

product transporting among the four components. The dotted arrows show the support from the four center platforms, including standardization support, knowledge support, network support, and security and data support by the blockchain. Table 1 identifies the differences between the traditional cloud manufacturing and blockchain based cloud manufacturing concepts. It can be seen that the blockchain technology enables a next-generation concept of cloud manufacturing by overcoming the critical challenges facing the existing cloud manufacturing systems. The proposed BBCM system adopts the blockchain technology to tackle the long-standing issues of cloud manufacturing, and empowers cloud manufacturing with several key capabilities as follows,

• Trust without a third party. The decentralized mechanism of blockchain allows each peer-to-peer node to have the full data which is inalterable and tamper resistant. As a result, the interaction among users is credible and traceable.

• Open and reliable. Take Bitcoin and Ethereum for example, the two most popular and successful blockchain systems have been successfully running worldwide on open source code models. The open and reliable property of blockchains is an important enabler for the BBCM system to attract more users to participate in the system. • Consensus standard. The consensus mechanism and open property of the BBCM system make it possible to define standards with full participation. • Easy payment. The crypto-system provides a convenient uniform method of payment for the global users. Moreover, the uniform payment system greatly facilitates the enforcement of the results of arbitration. • Value sharing. BBCM provides a pooled account and the grid computing reward mechanism which allow all participants to profit from not only their business but also the system growth. 2.2.1. Basic blockchain components The proposed system adopts a federated blockchain for cloud manufacturing. Two types of data are stored in the blocks, and they are transactions and smart contracts. Participants of the federated blockchain need to be approved so that they become creditable. There are many consensus algorithms in various blockchain systems such as proof of work (PoW), proof of stake (PoS), delegated proof of stake (DPoS), Ripple, and Tendermint [33]. In the two most popular blockchain systems, namely, Bitcoin and Ethereum, PoW is adopted as the consensus algorithm. The gas reward and block reward are divided into two parts. One part is for the miners who create the blocks, and the other part is saved into the pooled account of the BBCM system. Meanwhile, grid computing technologies enable users to share gas rewards and block rewards with the miners. Those who are involved in the system grid computing are rewarded by the pooled account. These mechanisms allow the federated blockchain to has more distributed characteristics and encourage more people to participate in the Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Table 1 Comparison of traditional and blockchain based cloud manufacturing systems. Traditional

Blockchain-based

Data storage

Internet-based traditional service

Blockchain, Oracle

System oriented

Service oriented

Consensus oriented

Payment

E-payment, Traditional payment

Uniform cryptocurrency

Security mechanism

Setting up security mechanism by each node itself or providing security service by the security firms [8,30]

Blockchain mechanism guarantees payment safety, and transaction safety. Oracle mechanism protects data from being stolen and juggled,

Trust

Identity check; authorization; isolation; access control; certificate-based authentication; user trust management; data and information flow tracing [31]

System mechanism provides trust. No independent trust is necessary.

Standard

No universal standards. Open communication standards proposed for research [11,32]

Consensus based global standard

Barriers for users

No barriers for all type of users

All providers and miners need to be pre-approved by smart contract vote.

Administrative tribunal

Offline local governments

Blockchain consensus mechanism-based arbitration committee

system. The design enables regular personal computers (PCs) to meet the computing requirement, and thus all participants can be involved in block creating. This in turn enriches the safety and vitality of the proposed system. Cloud manufacturing cryptocurrency (CMCC ) will be created in the proposed system. CMCC is a key for global payment, which can be generated by only two methods. One is allocating CMCC to the initial miners by a genesis file, and the other is creating CMCC with block rewards for miners. The genesis file is so called the first block. It includes the basic settings of a blockchain. However, the total number of CMCC is definite. Cryptocurrency exchange allows users to trade cryptocurrencies and conclude transactions. The users can make transactions, buy and sell cryptocurrency, and submit demand information. The cryptocurrency exchange interfaces with all services in the BBCM system. Oracle guarantees the safety and correctness of transaction data. It enables data to be transported peer-to-peer safely and correctly. In that way, the blockchain mainly saves the transaction information without large production files. Instead, the large production files are saved on the distributed servers by Oracle system. Before the files are loaded by a provider, the files are verified by a blockchain encryption algorithm. This design helps the blockchain system to reduce block file size and guarantee the data safety. Those advantages enable the blockchain system to have sustainable development ability. Decentralized applications (DAPP) allow everyone in the system to publish their unstoppable apps. The information of DAPP does not relay to a middleman for function or management. DAPPs can be applied to all kind of front-end applications of the proposed system. 2.2.2. Hierarchical structure of the proposed system As shown in Fig. 2, the proposed system is built on a hierarchical structure with six layers, namely, physical resource layer, perception layer, network Layer, blockchain layer, service layer, and application layer. The physical resource layer includes machines, resources and services which are used to finish the final user work. The perception layer is employed to achieve standardized work, which enables machines and services to serve for cloud users. All of the standards are subject to a consensus algorithm. The elements of this layer can be IoT hardware or middleware software. Network layer is based on the Internet and provides stable network service for the blockchain. This layer includes not only traditional network protocols, but also blockchain protocols, e.g., ripple transaction protocol (RTXP). The Blockchain layer provides transaction, confidence, and safety to the cloud manufacturing system. This layer includes blockchain, smart contracts, consensus algorithm, Oracle, exchange, and so on. In the existing cloud manufacturing systems proposed in literature, everything is treated as a service, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Based on the blockchain technology, the proposed system uses smart contracts to achieve the open source consensus standards. Therefore, in the proposed system, everything may be achieved via consensus. All services are subject to the standards and realized through the interface design. All interfaces are recognized in the proposed system because global developers abide by the same rules which are essentially designed by themselves. The service layer provides a significant number of standardized interfaces for application development, which are classified in two categories: blockchain interfaces and conventional interfaces. The blockchain interfaces include writing and reading interfaces, smart contract interfaces, consensus algorithm interfaces, grid computing interfaces, Oracle interfaces. The conventional interfaces contain virtual service interfaces, database interfaces, virtual interfaces, distributed computing interfaces, IoT interfaces, and standardization interfaces. Lastly, the application layer provides the interfaces of human–machine interaction for all participants. It includes applications and DAPPs. The representative applications of this layer are ERP, exchange system, demand system, manufacturing execution system (MES), supply chain management (SCM), and reservation system. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 2. Hierarchical structure of the proposed blockchain-based cloud manufacturing system.

2.2.3. Major operations A demand system guides users to submit their requests through the smart contracts. Oracle protects the demand information through an encryption and decryption mechanism. The CMCC exchange uses a smart contract to find a miner to organize the distributed computing to find the appropriate service providers. The distributed computing nodes will be selected and tested, and some computing nodes could even be selected from the manufacturing machines. All computing resources should be pre-approved by the smart contracts. In line with the result of task planning, the computing nodes visit the blockchain to acquire effective manufacturing resources. A recommendation algorithm is applied to achieve distributed computing, and determine the providers. As a result, the production can be scheduled, the cost can be estimated, and the pickup time and location of logistics can be obtained. In the system, CMCC virtual currency is used to pay the cost and Oracle is used to guarantee machine-to-machine trust. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 3. Schematic of solution computing for blockchain based cloud manufacturing.

Solution computing Fig. 3 illustrates the procedure of developing the computing solutions. The system will check whether a user has enough CMCC. If not, the system will guide the user to the CMCC-exchange system to buy CMCC. The next step is using a smart contract to select a miner to organize the computing. Communication between the miner and the user may happen regarding the details of job request, suggested price, and other issues. When the miner obtains all the information which meets the criteria of standards, distributed computing is organized by the miner and a solution will be obtained. Computers, graphics cards, machine computing units, and other devices which have spare computing power may be involved in the distributed computing. The distributed computing nodes also employ a smart contract. If the solution is obtained, the transactions is then completed between the demand side and provider. However, a job request is not guaranteed to be met due to various constraints. In this situation, the system will ask the user to modify the request with suggestions. Service Fig. 4 is the flow chart of service process which includes one or multiple production steps. The service providers prepare the parameters of production before writing the information to the blockchain through the smart contracts. After the authority of owner is received, the machine waits for tasks. The authority of owner means that the machine’s owner empowers the BBCM system to take over the machine through the smart contracts. The owner has to use the BBCM scheduling system to schedule tasks for the machine for avoiding schedule competition. However, since the jobs will be finished automatically, the machines are not connected to the system before the ‘‘authority of owner’’ information is obtained. IoT techniques allow machines to achieve certain level of intelligence. The machine characteristic information is recorded on the blockchain. That information can be updated through the smart contracts, but the historical data will be saved on the blockchain and cannot be modified. The open source recommendation algorithm needs to be voted before it can be deployed to the BBCM system. After the vote, a smart contract is created for recommending the service providers. In the meantime, based on the machine information and condition, the system generates the maintenance schedule. The maintenance companies also can be found based on the recommendation algorithm. All information changes will be written on the blockchain. Since each provider in the same task may spread over some distance, they are connected by logistics if necessary. Evaluation As shown in Fig. 5, after the final product is delivered, the users can evaluate the service on the system, and the service provider can view the user evaluation. An application of arbitration can be raised when the provider disagrees with the evaluation from the user. An arbitration committee votes on the case based on the process data stored in the blockchain. The result of arbitration committee voting is saved to the blockchain as the final result. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 4. Schematic of production steps in blockchain based cloud manufacturing.

Fig. 5. Schematic of evaluation process in blockchain-based cloud manufacturing.

3. Case study - 3D printing on blockchain based cloud manufacturing system 3.1. Scenario description Fig. 6 illustrates a scenario of 3D printing service through the proposed system. In the scenario, a customer (or user) requires the metal 3D printing technology to make a complex part. If the traditional practice is followed, the user will search for 3D printing service providers in certain regions or even globally, investigate the capabilities of service providers, compare their lead times and costs, and make a selection decision. This procedure not only is time-consuming and inefficient, but also has a few caveats. For instance, is the selected service provider really trustable? Or is the selected service provider the best choice? Those issues can be avoided if the proposed system is adopted. The user needs to submit a request on the system through using APPs/DAPPs. The request may include product specifications (e.g., geometry, material selection, surface finish, porosity, and mechanical properties), quantity, delivery time, desired processes, and machine specifications. After the user submits the request, a series of processes begin. First, when the company uploads the design files to the distributed server, a public key and SHA256 HASH are generated and saved on the blockchain. The key and HASH ensure that the files are not maliciously modified. Second, the system will calculate the workload stemming from the demand request, and estimate the cost. CMCC exchanges enable the user to easily buy the cryptocurrency. In the system, all serviceproviders are pre-approved by smart contracts. The qualified providers will be suggested by open source algorithms, which are more transparent and credible than many existing search engines. In addition, cryptocurrency is adopted to pay the transaction. After receiving the request and related expectations from the user, the system will select a miner to start the computing process by a smart contract, using an open source recommendation algorithm to develop the candidate solutions which includes 3D printing service and the related logistics. Typically, there are two options for obtaining the solution. One is a toll service which is provided by a powerful computing unit. The other is the distributed computing service free for users. The distributed computing enables the users to share gas fee and block rewards with the miner. This allows the system to accommodate more participants and to achieve high efficiency on large scales. All process data are saved on the blockchain for retrospective purposes. In the end, the service providers for 3D printing and transportation will be determined, and the user will be notified and may be asked for further information. Then, the selected provider for 3D printing service reads the design file from Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 6. 3D printing service based on the proposed block chain cloud manufacturing system.

a distributed service and verifies the file validity through Oracle server. The system will estimate the production time needed based on the order requirement and the specifications of the machine selected, in which the logistics time will also be accounted. After the customer receives the products, he or she needs to evaluate the services, and the results of evaluation will be written on the blockchain. If the service provider does not agree with the evaluation given by the customer, a request of arbitration may be submitted. All evidence comes from blockchain which guarantees authenticity and credibility of the evidence. During the process, voting will happen in the arbitration committee. This vote depends on data reports and collected through the smart contract. The vote result as the final result will be recorded on the blockchain, and the service level of the provider will be updated. 3.2. Simulation framework We set up the BBCM system based on a mainstream CoFBc, namely, Ethereum. The simulation is coded in Python 3.6 using Spyder IDE and runs on a Mac system with a 2.5 GHz Intel i5 CPU and 8 GB DDR3 memory. To simulate the real word cross-platform scenarios with different operating systems, three computers running the operating systems of MacOS, Ubuntu, and Windows respectively are employed. The computers are purposely located in different locations and connected to the Internet. In this CoFBc, eleven owners (can also be called miners) are determined, who can mine the blocks with the POA consensus algorithm. Meanwhile, there are 20 service providers that offer the 3D printing service. One hundred users are designed to generate demands. We employ a uniform distribution random function range from 8 to 10 to simulate the number of users’ job requests. In this simulation, a total of 939 job requests are generated, which approximately leads to an average number of 10 for the 100 users. The purpose of the simulation is to gain insights on the performance and evaluation of providers. For simplicity, we assume that in this case study, users do not rely on distributed computing for obtaining solutions for their service requests. Fig. 7 shows the simulation flowchart, with detailed description in the following sections. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 7. Simulation flow chart of BBCM for 3D printing.

3.3. Initialization of service providers and users A genesis file is created for the CoFBc to define blockchain properties. The miner’s addresses, used as the miner ID in blockchain system, are included in the genesis file. Weber et al. [34] discover a phenomenon that when a gas limit is exceeded, individual transactions could be delayed. While this observation may not be conclusive, we set the gas limit to a large number in this study to avoid the possible delay of transaction in simulation. As aforementioned, many consensus algorithms are available, and the popular Bitcoin and Ethereum systems employ the PoW consensus algorithm. However, the PoW algorithm tends to suffer from the majority attacks, while the PoA algorithm can avoid the problem in that it does not need to compete to create the new blocks [35]. In essence, PoA consensus is regarded as an optimized PoS that adopts the identity as the form of stake. Meanwhile, PoA consensus is generally favored on a permission chain in terms of efficiency and computational cost. The blockchain of the proposed BBCM system is a consortium blockchain which is a permission chain. Therefore, we employ PoA as the consensus algorithm. A unique address is assigned to each machine. The advantage of doing this is to allow different machines (even from the same company) to have their own service evaluation records. In the simulation, each 3D printer is regarded as a provider. Like any general bidding process, the customers/users need to specify product dimensions, material type, desired quality and properties (including surface finish/accuracy, strength), quantity, location and time of delivery. Also, the customers may also have a reserved budget limit for the job request. For simplicity, in this simulation we assume the material type suitable for all 3D printers is the same, the job quantity is always one, the accuracy/surface finish can be reflected by a single index s, and the mechanical properties can be reflected by another single index m. On the other hand, the information on providers that should be provided to the blockchain includes, but is not limited to, the perceived service level (PSL), the mechanical property index achievable, build rate, location, status of 3D printers, the accuracy/surface finish index achievable, and dimension limits (X/Y/Z) of builds. In the study, we assume the size of build requests is always smaller than the maximum build size among all the machines. Also, the product delivery time is in a normal range in the simulation by assuming all builds are small and the users are not sensitive to the delivery time as long as it is within a certain period such as one month. Moreover, PSL is assumed to follow a truncated normal distribution where µ = 6, σ = 2, and PSLi ∈ [0, 10], with 0 representing the worst and 10 representing the best. The location index of 3D printers is generated by a normal distribution which µ = 0, σ = 7. To estimate the total charge for a 3D printing job, the unit charge (u, measured by $ per printing volume) should be known. It is reasonable to assume that the unit charge increases with the increases in mechanical property and accuracy/surface finish requirements. Meanwhile, the unit charge should also increase with the maximum build rate of a 3D printer in that the capital investment and operation cost of a 3D printer of higher throughput are usually higher. As such, we assume that the unit charge can be computed as, ui = (B + β1 mi + β2 si + β3 bi )fiPSL

(1)

Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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where m donates the mechanical property index, s donates the accuracy/surface index, b donates the build rate, B is the base price of service, and β1 , β2 , and β3 are constants. In this simulation, β1 = β2 = β3 = 1. In the simulation, five CMCCs are assumed to be the base fair price, namely B = 5. Meanwhile, each company has its own pricing strategy, and this leads to the fluctuation of the unit charge. We assume the fluctuation follows a normal distribution, in which the mean equals the unit charge. Therefore, fiPSL is a normal distribution random function and designed to simulate this normal distribution which has µ = PSLi , σ = 0.8. This random function design effectively links the PSL to unit price estimation. On the other hand, we develop a scenario to calculate the expected charge for the job request from the user perspective. In other words, the user expects to pay a price based on the knowledge and information collected at hand. L = V · P · ρ,

ρ ∈ [0.9, 1]

(2)

where L is the user expected price for the job, V represents the volume of build, ρ is a correction parameter which reflects the variation of price sensitivity among users. An expected base unit price P ($ per printing volume) is designed to reflect the market fair price that the users are aware of. In the simulation, it is set to be 7. It is also assumed that when if the quoted price exceeds 1.5 times the user expected price, the quote will not be considered. 3.4. Recommendation of service providers K nearest neighbors (KNN) algorithm was developed by K. Nagendra Nath Reddy and Gabor Markus in 1972 [36]. In this study, the KNN algorithm is adopted to classify the providers. The providers are treated as the vectors which have multidimensional spaces. Each space presents a characteristic of a provider. Based on the classification, a provider list will be provided to the users. In the classification phase, the demand is treated as an unlabeled vector. Since the characteristics of a provider are normalized, the features of the vector are continuous variables. As such, Euclidean distance can be employed for the distance metric. In the simulation, we adopt a weighted nearest neighbor classifier to recommend a provider, and select four normalized characteristics: price of 3D printing service, mechanical property index, surface finish index, and reputation of provider. The price of 3D printing service (R) is computed by Ri = V · ui + l · d, i ∈ {0, 1, 2 . . . 19}, in which V represents the material volume of the job; ui represents the unit price of printing service; l represents the base logistic price which is set to be unity for this simulation; d represents the distance between provider to user. Clearly, other factors such as build rate and mechanical property index are reflected through their effects on u and R. In the simulation, we assume that the required time for the job only is effected by task volume and build rate. The four normalized characteristics are the four nearest neighbors in the KNN classifier. In the real world, users have different preferences ∑ towards the two characteristics. To reflect the preferences, weight wni is assigned to the ith nearest neighbor, with ni wni = 1, where n represents the number{ of nearest neighbors. For } ∗ n simplicity, we adopt the approach of Samworth [37] to obtain the optimal weighting scheme wni i=1 ∗ wni =

[

1 k∗

1+

d 2

d

−

{ 2

2

2k∗ d ∗ ∗ wni = 0 for i = k + 1, . . . , n.

⌊

4

2

i1+ d − (i − 1)1+ d

}]

for i = 1, 2, . . . , k∗ (3)

⌋

In Eq. (3), k∗ = Bn d+4 , where B represents a constant. This becomes the weighting scheme for computing Euclidean distance, DE , in the simulation according to Eq. (4), where Pj represents the jth neighbor value of provider, and Yj represents the jth neighbor value of job.

  k ∑ ∗ DE = √ wnj (Pj − Yj )2 ,

j ∈ 0, 1, 2, 3

(4)

j=1

3.5. Service evaluation Peterson and Wilson [38] indicate that the majority of Conceptual Distribution of Satisfaction Measurements follow a skewed distribution. Therefore, we employ a skewed distribution to simulate the user evaluation, with the assumption that the mode equals to the provider’s service level. Eq. (5) is used to compute the final system evaluation of providers (E).

∑k

ei ∗ γi Ei = ∑i k i=0

γi

(5)

where k denotes the number of jobs which the provider has completed; e denotes the evaluation score, and e ∈ [0, 10]; γ is the evaluation coefficient of user, and γi ∈ {0, 0.2, 0.4, 0.6, 0.8, 1}. The system will rate the user when an evaluation is submitted by the user. α is a user bias index of provider’s evaluation, which is evaluated by Eq. (6). In addition, we assume the value of γi can be computed by Eq. (7).

α = |ei − PSLi |

(6)

Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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⎧ 1, ⎪ ⎪ ⎪ ⎪ ⎪ 0.8, ⎪ ⎪ ⎨ 0.6, γi = Fun (α) = ⎪0.4, ⎪ ⎪ ⎪ ⎪ 0.2, ⎪ ⎪ ⎩ 0,

0≤α≤1 1<α≤2 2<α≤3

(7)

3<α≤4 4<α≤9 9 < α ≤ 10

Each provider’s evaluation (ei ) from users is simulated by a skewed distribution random process. The approach by O’Hagan and Leonard [39] is adopted.

δ (x) =

∫

x

1

√ −∞

2π

e−

x2 2

dt =

1

]

[

x 1 + ε( √ ) 2 2

(8)

where δ (x) donates the cumulative distribution function, and ε () donates the error function. As a result, the probability density function of the skew-normal distribution, f (x), can be described as, 1 x2 f (x) = √ e− 2 ∗δ (ax) 2π

(9)

where a donates the skewness coefficient. The usual transform x → ω is made to add location (ξ ) and scale (ω) parameters, which consider the shift and dispersion of the probability distribution, respectively [40]. By plugging this transform into Eq. (9), we obtain the final probability density function for service-evaluation as Eq. (10). x−ξ

f (x) =

2

√

ω 2π

2

e

− (x−ξ2) 2ω

∫

x−ξ a( ω )

−∞

1

√

2π

e−

t2 2

dt

(10)

In the simulation, an arbitration committee is designed. The probability of provider applying for arbitration is simulated. Fig. 8 shows the pseudo code of the arbitration algorithm. The assumed probabilities of 0.001, 0.1, 0.25, 0.75, and 0.95 correspond to the five situations where the service evaluation is (1) higher than the PSL, (2) lower than PSL with a difference less than 1, (3) lower than PSL with a difference ranging from 1 to 2.5, (4) lower than PSL with a difference ranging from 2.5 to 4, and (5) lower than PSL with a difference higher than 4. The process of arbitration follows a normal distribution random function in which the mean equals the PSL, and the variance is assumed to be 0.4. When mediation happens, the evaluation coefficient equals 1. This means the result of mediation has the highest weight on calculating provider’s score of service level. Note that a cost will incur when a provider applies for arbitration, and it is designed to be paid by the provider in this study. 4. Results and discussion 4.1. Simulation results In this simulation, 939 sequential job requests are generated. Among them, 934 requests are fulfilled by the proper providers, while 5 requests cannot be fulfilled. Fig. 9 shows the evolution of the provider service evaluation made by users. It can be discovered that the service evaluation scores of all providers stabilize with the increase of job index. This is because the reputation of each provider is set to 0 at the beginning, while the service evaluation from users becomes available once the first job request is served. It also implies that the outliers of user evaluation, if any, has only limited effect on the providers in medium or long term. In particular, we assign the average score of service evaluation to all providers that have not taken any job requests yet. This enables the providers new to the system to have the opportunity to compete for the jobs. Dramatic changes in service evaluation can be observed in the initial period for some providers such as Providers 6, 7, and 9. This is typically where the shift from an average score to the real evaluation occurs after a provider completes the first job. Fig. 10 illustrates the accumulated number of jobs undertaken by each provider with respect to the increase of job index. Fig. 11 summarizes the final total number of jobs received by the 10 providers. It can be seen that all providers have an overall growth trend. This is due to the fact that the job numbers of each provider accumulates with the increase of the job index. The providers that receive and fulfill the most number of jobs are Providers 7, 10, and 5, in ascending order. On the other hand, the curves are not perfectly linear, and exhibit multiple flat steps. The existence of steps on a curve indicates that the provider is not selected to take the particular job. In this simulation, 612 arbitrations in total are submitted, which accounts for more than 60% of the job requests. Fig. 12 only shows the first 150 jobs in which mediation take place. The blue curve represents the score given by the users, and the orange curve represents the score provided by the arbitration committee. It can be seen that the arbitration curve is generally higher than the user rating curve. This makes sense in that mediation will be automatically triggered when the customer rating of a job turns out to be much lower than the regular range of service level, which is supposed to be Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 8. Arbitration algorithm.

Fig. 9. Service evaluation evolution during the simulation.

Fig. 10. Number of jobs fulfilled by providers with respect to the job index.

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Fig. 11. Number of job requests fulfilled by providers.

Fig. 12. Service levels of the first 150 jobs that receive mediation.

detectable. In this case, the arbitration fixes the issue by increasing the service score for the provider. However, exceptions do exist. It can be discovered that the arbitration values are actually equal to or lower than the user ratings for the 10th, 36th, and 74th jobs. The implication is that the arbitration mechanism does not always guarantee the result of arbitration is higher than the user rating. 4.2. Discussion 4.2.1. Blockchain related overhead in the proposed BBCM system As mentioned earlier, the integration of blockchain technology and cloud manufacturing can significantly mitigate the issues of lack of trust, security, global payment, and standards. However, additional costs may be incurred in this proposed integrative solution. The additional costs are reflected various forms of overhead, such as transaction overhead, latency and confirmation time restrictions, and the cost of storage. Transaction overhead Fees will incur when users perform a cryptocurrency transaction, and different methods of transaction will incur different fees. Moreover, transaction fees are not only different in various blockchain systems but also change with respect to time. For instance, the average fee per transaction of Ethereum remained stable until September 2017, after which the average transaction fee increases and then fluctuates drastically between September 2017 to July 2018 [41]. A similar trend is also reported for Bitcoin, as well as other blockchain systems. The increase of transactions in unit time and the dramatic fluctuation of the cryptocurrencies are two root causes for the swing of transaction fees. Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 13. Estimated average number of transaction per second in the year index.

It is believed that the proposed BBCM system is faced with the similar risk of the increase of transaction fees. In the following, we discuss the estimations of the number of transactions, the changing price of CMCC, and the transaction fee in the proposed BBCM system. First, learning from the existing blockchain systems, the accumulative number of transaction can be modeled as Eq. (11) [42],

φ (T ) =

γ α + e(β−ζ .T )

(11)

where γ represents the limitation of the maximum transactions; α is a constant which equals 1; β and ζ are the transaction growth coefficients; T represents the time index in years. In the proposed BBCM system, it is impractical to handle more than 100,000 transactions per second since metal 3D printing is costly and thus the transaction is limited in numbers. Based on the recommended parameters settings [42] and our simulation tests, the estimated average number of transaction per second in the year index is estimated and shown in Fig. 13. Based on this estimation, the proposed BBCM system will experience a relative stable accumulation phase for the first 5 years, and then a rapid growth of the next eight years. The BBCM system is designed to maintain the stability of the CMCC price, by the mechanism of pooled account and value sharing. To estimate the price change of CMCC, for simplicity we only consider the inflation rate of the United States Dollar (USD) and the growth rate of global manufacturing. According to the Consumer Price Index of the Bureau of Labor Statistics, the USD price in 2018 is 54.05% higher than the average price in 1998, and thus during this period, the average inflation rate of the US dollar is 2.18% per year. According to the World Bank [43], the average growth rate of global T manufacturing is 2.76% per year from 1998 to 2017. Eq. (12) is used to calculate the USD price of the CMCC (PCMCC ), in which, pini is the price of the CMCC in the first year, i.e., pini = 100 in this study; rInflaction and rmanufacturing are the average inflation rates of USD and the average growth rate of global manufacturing respectively. T PCMCC =

T ∏

pini ∗ (1 + rInflaction )(1 + rmanufacturing )

(12)

i=1

By adopting the gas mechanism of Ethereum, we assume the price of a unit gas to the CMCC is a constant, and thus T the transaction cost can be estimated based on the estimated price of the CMCC, i.e., PCMCC . Fig. 14 shows the estimation of the minimum average overhead of a transaction in 20 years. It can be seen that the transaction overhead increases steadily with respect to time. Combined with the estimated growth in transaction number shown in Fig. 13, the total transaction overhead is expected to grow. Note that the fee of obtaining solutions is one type of the transaction overheads for the proposed system. For this new system, no similar models, prior experience, and reliable historical data can be referenced to suggest reasonable values for the fee. Also, as mentioned earlier, the current simulation does not employ the scheme of distributed computing for solutions. Therefore, this study does not address the fee issue quantitatively, and instead the above information on Bitcoin and Ethereum is borrowed for illustration. In a general sense, the acceptable fee of obtaining solutions is set by the service requester, similar to the transaction mechanism in Bitcoin and Ethereum. The service requester prepays this fee, but to undertake more jobs, the service providers may be willing to bear part of the cost. After the proposed system is deployed and sufficient knowledge is generated, the rational cost setting and/or splitting mechanisms can be established. Latency and confirmation time restrictions The latency of a transaction refers to the time taken from when the network receives an API call until it sends the confirmation such as conformance outcome, transaction hash, and block number [44]. According to literature [44,45], the transaction confirmation time is a good reflection of the latency of a transaction. For the existing mainstream blockchain systems, the latency of a transaction is affected by the number of transactions in the system, and the change rate of Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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Fig. 14. Estimation on transaction overhead in 20 years.

Fig. 15. Estimated confirmation time for the BBCM system in 20 years.

the number of transactions in the system. By taking these two factors into consideration, Fig. 15 shows the estimated confirmation time for the BBCM system. It can be observed that the confirmation time for a transaction is stable and low in the first five years. Recall that Fig. 13 shows that the number of transactions significantly increase after the sixth year and tends to level off after the sixteenth year. The confirmation time, on the other hand, shows a significant fluctuation pattern starting from the tenth year, in which the highest confirmation time is approximately 5800 min and the lowest confirmation time is less than one minute. Costs of storage In the proposed BBCM system, the blocks store the information of transactions, providers, and standards, and this also incur costs. The costs of blockchain storage include the expenses on storage medium and services, inter-cluster communication cost, and other expenses. It can be estimated the storage costs per GB for Bitcoin and Ethereum are about $22,766,000 and $4,672,000 respectively [42]. The similar nature of storage costs also exists in the proposed BBCM system. However, the BBCM system benefits from adopting a consortium chain structure and value sharing mechanism. The costs of storage will be significantly lower than those of Bitcoin and Ethereum systems. 4.2.2. Potential challenges of the proposed BBCM system Besides, other potential issues could arise for the proposed BBCM system, and solutions need to be developed in the future. The typical challenges are as follows, Privacy. A blockchain system uses a long digital address as an account which allows people to finish the transactions without exposing their private information. However, with the development of advanced tools such as machine learning and deep learning, the interconnection between the transactions in a blockchain system and real-life information can be learned and exposed. How to protect the privacy of the proposed BBCM system is an important research topic. Efficiency. As aforementioned, if the gas limited is exceeded, a delay of block generation may happen. With the increase of transaction in the system, the efficiency of the block generating will decrease. Moreover, the file size in each peer will increase to a size which cannot be afforded by the users. Participation. There are a few failed cases of cloud manufacturing systems worldwide. One main reason for the failures is the lack of active users thanks to the concerns on trust and safety. With the proposed BBCM system, the risk associated Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.

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with the lack of users should be reduced with the value sharing mechanism. However, the risk still exists, in particular, in the initial phase of BBCM implementation. In this case, the system will face situations such as the dramatic fluctuating price of the CMCC and unsuccessful match between service providers and user requests. As such, innovative solutions are called for to mitigate this challenge. 5. Conclusions In this research, we propose a novel concept of cloud manufacturing which combines blockchain technology and cloud manufacturing. The concept overcomes the weaknesses of existing conventional cloud manufacturing systems. The proposed system has three essential features inherited from blockchain technology, namely, consensus-oriented, global payment, and creditable distributed mechanism. Based on the structure of Ethereum blockchain, we construct a federated blockchain system for the purpose of cloud manufacturing, and perform a simulation study on the scenario of 3D printing services. In the simulation, three types of roles, namely, miners, providers, and users are designed. Smart contracts are created with solidity language to read and write data and transaction on the blockchain. A recommendation algorithm is built in to select the proper service provider upon receiving a job request. An arbitration mechanism is introduced to provide a fair evaluation to the provider’s service, should disagreement between the provider and the user occurs. For 939 job requests generated, 934 requests are fulfilled by the appropriate 3D printing service providers, while 5 requests cannot be fulfilled. Meanwhile, more than 60% of the jobs after service end up with arbitration. The simulation demonstrates that the concept of the proposed system is feasible. Although integration of blockchain technology and traditional cloud manufacturing overcomes most of the issues of conventional cloud manufacturing designs, more detailed work needs to be conducted in the future. The areas of research extension include the mechanism of setting up initial blockchain owners, the design of a unique cryptocurrency system for the proposed system, the adoption of distributed computing and grid computing techniques in the system, the implementation of cybersecurity measures, the improved cost structure of computing a manufacturing solution, and the cost split between the end-users and the manufacturers, and so on. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Please cite this article as: X. Zhu, J. Shi, S. Huang et al., Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study, Pervasive and Mobile Computing (2020) 101113, https://doi.org/10.1016/j.pmcj.2020.101113.