a new era of web collaboration: cloud computing and ...

A NEW ERA OF WEB COLLABORATION: CLOUD COMPUTING AND ITS APPLICATIONS IN MANUFACTURING UDC: 004.89; Dimitris Mourtzis 1, Babis Schoinochoritis 1, Ekaterini Vlachou 1 1 Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26500, Rio Patras, Greece [email protected] Paper received: 18.02.2015.; Paper accepted: 03.03.2015. Abstract: A major transition in the way that digital tools are utilized for business collaboration can be observed over the last few years. The huge growth in the amount of collected data and the massive development of mobile computing and the Internet of Things have driven the vast majority of modern business activities to be performed under the umbrella of cloud environments. As this transition is expected to bring significant gains in quality of life, employment and operational efficiency, the manufacturing sector has already began to incorporate the cloud business paradigm, with applications in different areas namely, product development, manufacturing process, and manufacturing systems and networks management. Quality aspects are a major consideration when designing and implementing such systems. Certain approaches are followed, so that their performance, namely the quality of service, is retained at high level throughout their lifecycle. The transition into this whole new era of networked computing and its impact on transforming the manufacturing sector are discussed in this paper. In addition, the conceptual model of a novel cloud manufacturing platform is presented. Key Words: Cloud Manufacturing, Quality of Service, Security.

1. INTRODUCTION Cloud computing has already managed to spread across many different industries and numerous users around the world despite the fact that it is clearly a philosophy born in the 21st century, being a natural outcome of globalization [1] and the shift of industrialized countries towards a more service-oriented society model [2]. A report published by the Australian Government [3] indicates that almost one fifth of the Australian enterprises were already using cloud services in 2011, while the majority of the remaining enterprises were planning to launch cloud services by 2013. This burst in the use of cloud computing during the last years can be attributed to two different factors: i) the advancements in the associated technologies and ii) the benefits that cloud computing brings. The development of technologies such as mobile computing [4] and trends such as the Internet of Things (IoT) [5], [6] have created an ideal environment for cloud computing to flourish, practically rising the geographical limitations that

accompany the typical desktop computing model. The evolution of cloud computing has been proved to be beneficial for modern economies. For example, it can boost employment by the creation of new jobs [7] and increase productivity by simplifying business operations and enhancing collaboration [8]. A massive increase in the amount of collected data, leading to “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze” [9], known as Big Data, can be observed in the last years [10]. However, the effective exploitation of Big Data encloses great challenges and requires novel approaches and tools in order to maximize impact [11]. This has driven to the development of cloud solutions that can handle this data effectively with the use of smart approaches, known as “analytics”. Finally, it has been stated that cloud computing is particularly beneficial for small and medium enterprises (SMEs), since its use can help cutting down costs [3], giving them the opportunity to innovate and retain their position among the global competition [12].

IoT SMEs QoS CAD CAE XML

Internet of Things Small and Medium Enterprises Quality of Service Computer aided Design Computer aided Engineering Extensible Markup Language

SaaS ISF IC IT OWL MES

Software as a Service Incremental sheet forming Integrated Circuits Information Technology Web Ontology Language Manufacturing Execution System

STEP CAx IaaS PaaS

Standardized Graphic Exchange file Computer aided technologies Infrastructure as a Service Platform ad a Service

ERP SPN VPN

Enterprise Resource Planning Stochastic Petri Nets Virtual Private Network

11

Manufacturing System Knowledge Discovery

Data Mining

Knowledge Reuse

Predictive Modeling

Product Development

Internet of Things CyberPhysical systems

Forecasting Decision Support

Years

Non-linear process planning

1990

Standart Reporting

Operation Intelligence

Discrete Event Simulation

Machine learning

Manufacturing Processes

2000

2010

UserActivity Rating

Yield Management

Real-time Analysis

Data Mining

Data Warehouse

Manufacturing

Manufacturing systems Management

2020

Social Media Contextual marketing

Figure 1 Technology evolution over the last 30 years Based on the above, the concurrent evolution of Big Data and analytics, with respect to the different stages of product manufacturing, is schematically represented in Figure 1. As the global competition in the manufacturing sector is constantly rising [13] and altering forms [14] and also exploitation of Big Data can dramatically increase manufacturing efficiency [15], [16], a totally new [17] manufacturing paradigm, namely the cloud manufacturing, is gaining attention over the last years. As a result, the challenges of the integration of distributed manufacturing resources and the cooperation and information exchange among different manufacturing sites as well as departments and organizations can be tackled in an efficient way [18]. Since cloud infrastructures are becoming more and more essential components of modern manufacturing systems and can be hardly distinguished from components of other types (e.g. machinery), forming what is called cyber-physical systems [19], their quality and reliability is significantly influencing the overall performance of the systems. The quality that characterizes a networked computing system is evaluated using a domain-specific metric, namely the quality of service (QoS). Therefore, QoS is a crucial factor to be taken into consideration during the conception, design and implementation phases of cloud manufacturing systems.

The aim of this paper is to investigate the evolution, advances, current applications and the future trends of cloud manufacturing. Moreover, the significance of the QoS and security issues, as far as it regards the impact they have on cloud manufacturing systems, is also discussed. Finally, after an insight into the aforementioned research areas has been gained, a novel and conceptual cloud manufacturing framework is proposed.

2. REVIEW METHODOLOGY The literature review for the scope of this paper was performed in three stages: i) search in the Scopus database [20], ii) identification of relevant papers by abstract reading and iii) full-text reading and grouping into research topics. Three different search sessions were run. Firstly, the records that included within their title both the words “cloud” and “computing” were found. The total number of records was 6909 with the oldest article published in 2003. The second search performed was for records including in their title the exact phrases “cloud technology” or “cloud technologies”. The total number of records found is 76 and the oldest article is published in 2009, six years after the first “cloud computing” publication. Finally, a search focusing on cloud computing in manufacturing was performed.

12

Frequency of appearance

Publications per year (Scopus)

Publications per type (Scopus) 1.40% 0.36% 0.29% 0.25% 0.09% 1.47% 1.30% Conference Paper 1.34% Article 2.05%

2000 1500 1000 500

Editorial Conference Review Book Chapter Article in Press Review Note Book Short Survey Letter

25.29% 66.16%

0 2008 2009 2010 2011 2012 2013 2014 Year

Publications per type (Scopus)

3%

1%

Frequency of appearance

1% 3%

Publications per year (Scopus)

Computing

Conference Paper Article Book Chapter

27%

65%

Review Editorial Note

100 90 80 70 60 50 40 30 20 10 0 2008 2009 2010 2011 2012 2013 2014 Year

Frequency of appearance

Publications per year (Scopus)

Publications per type (Scopus)

25

1%

1%

1%

0%

20 15 10

49%

5

48%

0

2009

2010

2011

2012

2013

2014

0% Conference Paper Article Article in Press Review Editorial Note Short Survey

Year

Figure 2 Cloud Computing, Technology, and Manufacturing papers frequency per year and type The records here should include in their title both the words “cloud” and “manufacturing”. The total number of records found is 319. The oldest article is published in 2008, five years later than the oldest “cloud computing” article. Thus, it can be stated that cloud computing had reached a certain level of maturity prior to being applied in the manufacturing sector. The number of publications per year from 2003 until 2015 is shown in the diagram of Figure 2 along with the type of record (e.g. journal article, conference paper, etc.) distribution. It can be clearly observed that a radical increase occurs in 2009 for the term “cloud computing” and the yearly number of publications then grows steadily until the year 2013. The other two terms are following a similar trend. Regarding the type of the records, it can be observed that in the “cloud computing” and “cloud technologies” records, the majority of them are conference papers. On the other hand, in “cloud manufacturing”, an equal distribution between conference papers and journal articles is observed. The above observations are depicted in Figure 2.

2.1 Definitions Hereby, the definitions of cloud computing, cloud manufacturing and QoS are presented and adopted for the scope of this research work. “Cloud computing is a model for enabling ubiquitous, convenient, ondemand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction” [21]. “Cloud manufacturing is a smart networked manufacturing model that embraces cloud computing, aiming at meeting growing demands for higher product individualisation, broader global cooperation, knowledge-intensive innovation and increased marketresponse agility” [22]. “QoS denotes the levels of performance, reliability, and availability offered by an application and by the platform or infrastructure that hosts it” [23].

13

3

CLOUD COMPUTING APPLICATIONS IN MANUFACTURING

data as XML files through the application of the STEP-XML protocol. The ability of the proposed system to overcome the limitations of the standard STEP standard was tested by application in a manufacturing case study. A novel STEP-based data model is proposed by Li et al. [26] aiming to increase interoperability and compatibility between different CAx systems in cloud manufacturing environments. The new data model can represent four different domains of product knowledge, namely the customer, product, manufacturing and resource domains. The case studies indicated that the modeling methodology is characterized by high completeness, interoperability and compatibility. A cloud architecture combining collaborative design, integrated manufacturing and supply chain management is presented by Wu et al.[27], [28].

Cloud manufacturing can enable the creation of intelligent factory networks that offer ubiquitous information provision. Cloud computing systems and cloud manufacturing may play a critical role in the realisation of “Design Anywhere Manufacture Anywhere” philosophy [22]. Cloud service models used so far in a number of scientific works are classified as Software as a Service, (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Three distinct fields of application for cloud computing in manufacturing, namely product development, process optimization and manufacturing systems management can be observed from the literature review. The distribution of the publications in these three fields of application is presented in Table 1. In addition to that, the immediate and long term benefits that cloud manufacturing brings in each field are illustrated in Figure 3. More specifically, during the product development phase, cloud manufacturing approaches can offer strong capabilities for collaborative design by enhanced coordination of design activities performed by users across the globe and integration of multiple CAx tools, leading to improved operational efficiency. By using cloud technologies, a manufacturing task can be executed in a selection of manufacturing resources distributed around the world and also a proper matching between tasks and resources can be achieved in order to increase sustainability and flexibility. As far as it regards manufacturing systems management, cloud technologies are increasing productivity, reduce the time-to-market and also can lead to enhanced enduser’s experience by raising product quality. The benefits and the gaps of applying cloud computing in the three manufacturing fields defined above, are defined and analysed in the following sections.

Manufacturing Processes

Product Services Development

Cloud Immediate benefits Improved Efficiency

Manufacturing systems Management

The development of a product is at most cases encompassing the application of digital methods such as computer-aided design (CAD) and computer-aided engineering (CAE). A step forward is the integration of the digital tools related with product development into cloud platforms in order to enhance collaboration among the various actors of this stage of the product lifecycle. Product-services are introduced to deal with dynamic interdependencies of products and services in production, including product design and product service developments in the production [2]. Productservice systems are highly related with cloud architectures, as the latter consist of a number of services in a cloud environment. The potential of cloud technologies in collaborative design is welldiscussed in [24]. Fatahi Valilai and Houshmand [25] have proposed a cloud platform that can be used for design optimization and is handling the geometrical

Collaborative Design

Reduced IT Costs Scalability, Agility Ubiquitous Data

Real-time Quoting Flexibility, Adaptability Reduced Cost

3.1 Product development

Cloud LongTerm benefits

Increased Productivity

Sustainability

Distributed process planning and manufacturing

Ubiquitous Data

Reduced Time-to market Enhance end user’s experience Resource pooling and sharing

Enhanced Collaboration and communications between different systems

Figure 3 Immediate and long-term benefits of cloud manufacturing. The architecture is tested on the development and launch into the market of a small delivery drone. An ontology-based method for modeling of design services in cloud manufacturing environment is proposed by Liu and Jiang [29] and implemented in a cloud system for missile development. Implementation results showed that the proposed method is feasible but lacks effective cooperation between the different services and also non-executable workflow files are often generated. A methodology for combining different cloud

14

services during product design, in case there is no individual service that can perform a design task, is investigated by Gao et al. [30]. A cloud platform, namely the CloudSME Simulation Platform, is introduced in [31]. The platform can be utilized for deploying simulation tools, which can be used for product design such as the Ingecon’s 3D Scan Insole Designer, as a web service. In order to increase the efficiency of collaborative design activities, a novel energy adaptive immune genetic algorithm, which is used for scheduling of design tasks in cloud manufacturing systems, is proposed by Laili et al. [32]. The aforementioned algorithm is advantageous in terms of solution optimality, if compared with the typical immune genetic algorithm. On the other hand, the computational time remains the same.

planning and G-code generation required for the machining of a given part. A considerable amount of research effort has been devoted to the modelling of physical manufacturing resources so that they can be represented as services into cloud systems. Zhu et al. [39] developed a bilayer resource modelling methodology. In the Cloud End layer, the basic resource model, which contains information regarding resource characteristics such as physical structure and geographic location, is established. In the Cloud Manufacturing Platform layer, service information, such as input and output data templates and function parameters, is stored. Liu et al. [40] proposed a method for representing the capabilities of manufacturing resources that associates them with the geometrical features of the products. Their approach is advantageous for cloud manufacturing platforms, as it enables efficient process planning over multiple manufacturing facilities. A resource virtualization methodology, which makes use of the OWL-S ontology language, is introduced in [41]. The methodology is applied for virtualizing a large China-based aerospace company, with the findings indicating that the use of the OWL-S can be proved effective for multiple-aspect modelling of resources. The OWL language is utilized by Wang et al. [42] for the modeling of manufacturing resources in cloud environments.

3.2 Manufacturing processes The application of cloud computing in order to control and optimize manufacturing processes in terms of cost, time and energy consumption has been investigated by a number of researchers. Paniti [33] investigated the implementation of the incremental sheet forming (ISF) process as a service into a cloud platform and the optimization of the process with a tool path adaptive control algorithm. The algorithm is capable of adjusting the forming depth in order to prevent fracturing of the sheet due to localized thinning. Tapoglou et al. [34], [35] proposed a methodology for determining the optimal process parameters in order to minimize cycle time in machining processes. The methodology is making use of IEC 61499 function blocks in order a cloud implementation to be feasible. The effectiveness of the methodology is demonstrated through a face milling case study, with the results indicating that it can be utilized for fast on-board optimization of the process. IEC 61499 blocks were also utilized by Wang [36] in order to enable machine availability and execution status monitoring of metal-cutting machines from a cloud platform. Based on this approach, a web-based platform for distributed machining process planning was built, capable of performing decision-making under unpredictable changes in the shop-floor. A cloud solution for remote access and control of manufacturing equipment is presented by Wang et al.[19]. The proposed system is applied in the remote control of a robotic assembly cell. The physical robot can be remotely controlled by the operator who manipulates a virtual robot instead. Park and Jeong [37] developed a cloud system for identifying and managing faults in integrated circuits (IC) manufacturing environments, with the system being tested in an IC test handler machine. The findings of the research showed that the operational efficiency is increased by reducing repair time, strengthening the Green IT character of the system. In the platform proposed by Fatahi Valilai and Houshmand [25], [38] cloud services are used to automate the process

3.3 Manufacturing systems and networks management Cloud computing is considered as an essential step towards the effective management of modern manufacturing systems and networks which broadly are geologically decentralized and therefore the integration of distributed manufacturing resources is of critical importance [18]. Therefore, a great interest, on how the cloud manufacturing paradigm enhances the management of manufacturing systems, has been developed among researchers. A cloud manufacturing system that provides resource scheduling in order to minimize energy consumption and manufacturing costs is proposed by Cheng et al. [43]. A scheduling methodology in a cloud manufacturing environment is proposed by Lartigau et al. [44] as well. Energyefficiency is also a matter of consideration in the cloud production planning system which is investigated by Um et al. [45]. A cloud-based manufacturing execution system (MES) that moves beyond the barriers, especially as far as it concerns SMEs, set by the current enterprise resource planning (ERP) enhanced user experience and strong decision-making capabilities that maximize the benefits of IT systems. Cloud manufacturing platforms specifically targeted to SMEs are also demonstrated in [47] and the article by Huang et al. [48]. Challenges towards the further adoption of the platform by industrial users involve management and load-balancing issues among heterogeneous computing clusters and also the efficient detection of software faults.

15

Table 1. Literature distribution in fields of Cloud manufacturing, QoS, and Security

Papers

[24][25][26][27][28][29] [30][31][32] [33][34][35][36][37][38] [39][40][41][42] [43][44][45][46][47][48] [49][50][51][52][53][54] [55][56][57][58][59][60] [61][62][63][64] [22][67][68][70][71][72] [73][74][75][76][77][78]

Product Development

Cloud in Manufacturing Manufacturing Sectors MFG Systems & Manufacturing networks Processes Management

QoS

XXX XXX

XXX

XXX XXX

[38][86][87][88][89] [25][29] [25][38]

Security

XXX

XXX XXX

XXX XXX

[48]

The MES presented in [49] aims to improve assembly processes quality by realizing seamless flow of information at all the levels of manufacturing enterprises, from assembly stations to policy-making offices. A model which makes use of stochastic petri nets (SPNs) is proposed by Wu et al. [50] in order to synchronize the material flow of crowdsourcing processes in cloud manufacturing systems. Results of the research showed that the model can provide insight of the material flow dynamics to system designers. However, computational problems may be faced when complex material flows are modeled and also validation through application of the model in a large-scale industrial case study is required. A methodology that utilizes the improved particle swarm optimization (IPSO) algorithm is presented by Wang et al. [51] for optimally allocating machining tasks to resources. Computational results showed that the proposed IPSO algorithm is advantageous if compared with a version of the well-established genetic algorithm (GA). The optimal allocation of orders to geographically distribution using an intelligent cloud-based methodology is discussed in [52]. The methodology was implemented in a cloud system, which was tested in a Hong Kong-based apparel manufacturing company. The results showed that the system offers enhanced capabilities of monitoring and analyzing the production flow and also leads to decreased costs, defect parts and increased production efficiency by better work-load balancing. An IoT approach to cloud-based production logistics planning is introduced by Qu et al [53]. The proposed approach was implemented in a paint factory which consisted of 19 palletizing points and 2 warehouses. An IoT-based cloud manufacturing concept is also presented by Tao et al. [54].

16

XXX

A cloud manufacturing platform especially designed to enable collaboration in the large equipment manufacturing industry is presented in [55], while the platforms introduced in [56] and [57] are designed for application in the shipbuilding industry. Chen [58] investigated how sustainability of semiconductor manufacturing can be enhanced through utilizing cloud computing. Wang et al. [59] proposed a cloud system for managing Waste Electrical and Electronic Equipment (WEEE) remanufacturing systems. The system is tested by application in the remanufacturing of an LCD TV. In the system proposed by Qanbari et al. [60], cloud services are deployed at portable devices. The effectiveness of the dashboards in cloud manufacturing systems management applications is discussed in [61]. The management of an apparel industry is performed under the social manufacturing paradigm in [62], in order to deal with the complexity deriving by the mass customization trend. Social manufacturing is also investigated in [63]. The application of cloud manufacturing for addressing the challenges of highly-customized products is discussed in [64].

4. QUALITY OF SERVICE IN CLOUD MANUFACTURING QoS has been identified as one of the most important aspects in cloud computing [65] and also as a key requirement for the further adoption of cloud manufacturing [18]. The fact that QoS is fundamental for both cloud users and cloud provider [66] has prompted several researchers to investigate quality issues in cloud manufacturing systems and established QoS management as an essential function of cloud

manufacturing platforms [67], [68]. QoS management encompasses systematic monitoring of resources, storage, network and virtual machines [22]. While QoS in simple networked systems mainly depends on the available bandwidth [69], QoS in manufacturing clouds is relying on other factors as well. In the framework proposed by Lu et al. [70], QoS mainly depends on the proper selection of manufacturing resources that will satisfy a manufacturing request. Tao et al. [71] proposed to measure the quality of a software or a hardware service, according to its duration, cost, energy consumption, reliability, maintainability, trust and function similarity. In the semantic modeling approach, followed by Wang et al. [72] , the QoS model includes information about due time, service price, service level and quality demand. On the other hand, in the approach followed in [29] QoS is modeled as a function of time, cost, quality and creditworthiness. Lartigau et al. [73] presented an approach to service composition which models QoS as a function of cost, time, reliability, maintainability, availability and environmental impact. QoS acts as a constraint when deploying and scheduling both virtual and physical simulation tasks in the platform proposed by Laili et al. [74]. The QoS function in the cloud manufacturing system investigated in [1] is used to prevent network delay and obstruction. QoS is the ultimate criterion for the multi-task services composition methodology investigated by Liu et al. [75]. In [76], the QoS is combined with energy consumption, as objectives, in order to determine the optimal service composition. A discrete hybrid bees algorithm is employed by Tian et al. [77] for the optimal service composition under QoS constraints. The algorithm was found to perform better than an existing multi-objective genetic algorithm. Jiao et al. [78] have presented a methodology for computing the latent semantic similarity of fuzzy clustering for the QoS.

5. SECURITY Despite all the advantages of cloud technologies, including decreasing of IT costs and enabling ubiquitous computing, cloud security is currently a major concern, which decelerates the growth of cloud computing [79]. Security and data protection are challenges generally considered in conventional communication and information exchanging systems, but also apply to cloud systems. However, the ways of dealing with them need to be reconsidered with respect to their suitability for cloud [80]. Additionally, cloud computing has introduced a number of new threats and concerns. Issues such as resource location, multitenancy, system monitoring and authentication need to be tackled in an integrated manner [81]. A categorization of the existing cloud threats including account control, multi tenancy issues, malicious insiders, data control, and management console security is presented by Zissis et.al. [82]. A presentation of cloud security threats is also performed in [83], where cloud

17

abuse, insecure application programming interfaces, malicious insiders, data loss and account and service hijacking are considered as the main threats. A more abstract taxonomy of cloud challenges is presented by M. Ali et al. in [84]. Cloud security is divided into three main categories, namely communications, architectural and contractual security. Based on the different aforementioned approaches in cloud security issues, a generic categorization is presented in Figure 4. Cloud Security Challenges

Communication Security

Architectural Security

Legal aspects

Virtual Networks

VM issues

Legal issues

Common Infrastructure

Data Control issue

Account and Service hijacking

API issues

Access Control issues

Figure 4. Abstract taxonomy of Cloud Security Challenges [82][83][84] Security issues in cloud manufacturing systems can often lead enterprises to consider restricting the cloud functions to local private networks, without any access to the web [84][85]. As a result, many capabilities and benefits of the cloud manufacturing paradigm cannot be exploited at all. Various approaches exist in the literature that counter the security issue and propose multiple solutions (Figure 5). An advanced cloud protection system is introduced in [86] trying to tackle a number of challenges by providing more secure cloud resources. In [87] a cyber-security network is presented called CyberGuarder. The latter uses a two-layer tunnel Virtual Private Network (VPN) between virtual bridges. An XML security service was implemented by Fatahi Valilai et al. [38] for the verification of manufacturing agents to data structures in the cloud platform they present. A security layer in a cloud manufacturing platform, providing data security along with multi-agent security, is presented in [88]. As far as it concerns commercial cloud security software, a report published in 2011 indicates that the respective market has a growth rate of 25% and was expected to have a size of $963.4 million in 2014 [89]. Key players in the market include Trend Micro Inc., McAfee Inc., CA Technologies and Symplified Inc., with their collective market share being almost 40% [89].

to a large computing infrastructure, where users can deploy their own virtual machines by using the Amazon Web Services [95]. Cloud services provided by Amazon have been already in use by Automobili Lamborghini and Unilever [96]. As it can be observed, there are numerous commercial cloud services providers. Moreover, the services they offer can be of very diverse nature. As a result, a methodology to evaluate the performance of commercial cloud services is needed in order to support decision-makers for which platform their company shall adopt. Li et al. [97] have presented a taxonomy of the performance characteristics of commercial cloud services in order to facilitate their analysis. On the other hand, open- source cloud platforms such as OpenStack, CloudStack and Eucalyptus are advantageous in terms of providing access to numerous development resources, quicker updates and preventing vendor lock-in [98] Major manufacturing firms such as BMW [99] and Tata [100] as well as well-established research and academic organizations such as Inria and the University of Melbourne [100] are currently using cloud platforms based on the Apache CloudStack.

User

User Authentication

User

Communication Protocols Services- Applications Service 1

Service . . . Service 2 n

Virtual Machines Access Control

Databases

Legal Issues

Factory 1 Company Factory 2

7. CONCEPTUAL FRAMEWORK

Figure 5. Cloud security issues from industries to the end user. The challenges for the cloud security market involve the limited awareness for the security threats associated with cloud computing and also the lack of established cloud security standards. In order to deal with those challenges, the market has been reported to transform into a model which is characterized by increased collaboration between cloud service providers security solution providers and strong development cloud security standards which will also serve the need for evaluation of the commercial solutions by the customers [89].

6. TECHNOLOGY REVIEW Several major IT companies have been introducing commercial cloud solutions to the market over the last years, following the increasing demand for high-quality cloud platforms [90]. Microsoft is collaborating with Invensys in order to host cloud-based manufacturing applications on the Windows Azure cloud platform [90]. The hosted applications are claimed to enhance collaboration, reduce costs and ease IT management. The IBM cloud computing platform is already adopted by 25 Fortune 500 companies [91], while the Google Cloud Platform is used by Coca Cola, Best Buy, Sony Music, Ubisoft among other major multi-national companies [92]. The Plex Manufacturing Cloud has been successfully and brought a number of positive shift in the way that major US-based manufacturing companies, such as Cast Aluminium Solutions [93] and Avon Gear Company [94], operate. The Amazon Elastic Compute Cloud (EC2) is a platform that provides access

18

The main requirements and challenges of a cloud manufacturing environment can be summarized in Big Data support, real-time operation, configurability and agility, security, cyber-physical systems, social interaction and finally QoS. These requirements drive the development of new architecture for cloud manufacturing. Large and medium manufacturing enterprises, following the trend of 2020 technologies, require a new architecture of cloud capable of managing Big Data, supporting real-time operations and considering social interaction. Following these requirements a cloud manufacturing framework is presented, which consists of a number of critical technologies able to satisfy the requirements. Cloud technology is a critical characteristic to support dynamic configuration of the companies’ services in order to suit in different conditions. Cyber-Physical systems are another core characteristic in the conceptual architecture capable of processing big amount of data and performing real – time operations. Moreover, combining cloud and cyberphysical technologies will lead to a more secure design with higher quality of services [101]. Finally the social interaction and feedback analysis can be achieved quickly and efficiently, as the cyber-physical system will manipulate the social data and the cloud technology will enable ubiquitous access to data. The use of social media will benefit the communication between the different systems and will lead to active and informed customers and industries. More specifically, modern industrial shop-floors, by incorporating the novel conceptual architecture, will introduce a new common information flow.

Industries

Customers

Cloud Infrastructure Applications & Services

Servers

Big Data Processing

Manufacturing Requirements

Feedback

Cyber- Physical Shop-floor Smart Wireless Sensor Networks Simulation

Data Management

Physical Shop-floor

Figure 6. Schematic of Conceptual Framework Smart Sensor Networks together with real-time simulation will introduce a new generation of cyberphysical systems that will enable quick and accurate information flow providing feedback and other systems and finally to the end-users. Finally, the cloud environment in combination with the cyber-physical system will introduce a new way of manufacturing. A conceptual framework, addressing all the aforementioned needs, is illustrated in Figure 6.

8. CONCLUSIONS An extensive review of the cloud manufacturing literature has been performed in this paper. Cloudcomputing, web collaboration, IoT, virtualized resources and social communications have been identified as key enabling technologies that are transforming the conventional product-oriented manufacturing business model to a service-oriented one. Cloud computing has been applied in a number of manufacturing functions. product development, manufacturing processes and manufacturing systems managements are the three major functions that are highly influenced by cloud technology. Whereas the use of cloud has been beneficial in terms of decreasing production cost and time and also enhancing product quality, there are still barriers to its

19

further adoption. In product development, collaboration among the different services can lack efficiency in certain cases. The modelling and virtualization of manufacturing resources is performed under several different approaches. A standard methodology has not been established yet. This fact leads to reduced interoperability and puts obstacles to the collaboration between different cloud platforms. Finally, when it comes to the management of large manufacturing systems with complex workflows, the computational efficiency of cloud-based MES is often not sufficient. Additionally, there are issues of computing nature that need to be addressed as well. As cloud infrastructures are becoming vital parts of the actual manufacturing systems, the QoS should be kept at high level, preventing bottlenecks in the systems. In addition, due to the fact that manufacturing data is at most times confidential and of high value, security vulnerabilities and threats emerging from the nature of networked systems, necessitate a systematic investigation of cloud manufacturing security issues. Furthermore, the IT market seems to rapidly adapt to the rising popularity of cloud manufacturing. Several turn-key solutions have been introduced by wellrenowned IT firms and implemented in major manufacturing companies. Beyond commercial solutions, several open-source cloud platforms with manufacturing applications exist as well.

Table 2. Indicative Challenges, Opportunities and Barriers of Cloud computing application in Cloud manufacturing Cloud Manufacturing Opportunities Agility, flexibility and adaptability[3]

Barriers Data security[10]

Security[18]

Manufacturing time reduction[1]

Industry data ownership[80]

High QoS[18]

Manufacturing costs reduction[13]

Loss of control[80]

Cost saving[35]

Manufacturing systems sustainability and efficiency[27]

Performance and capacity requirements [50]

Computational capacity[52]

Distributed manufacturing environments[13]

Privacy concerns[27]

Effective modelling of physical resources [39]

Efficient collaboration within global manufacturing networks [13]

Bandwidth of network connected to the cloud [48]

Challenges Big Data[11]

In conclusion, there is a remarkable number of applications of cloud computing in the manufacturing sector. However, there are still unresolved issues, including security, QoS, as well as the use of standard communication protocols. Cloud manufacturing is progressing towards addressing the future challenges described above. Therefore, academic and commercial research should focus on designing new cloud architectures capable of taking advantage of the opportunities and dealing with the challenges and the barriers, as outlined inTable 2.

ACKNOWLEDGEMENT The work presented in this paper is partially supported by the EU funded research project “Collaborative and Adaptive Process Planning for Sustainable Manufacturing Environments – CAPP-4SMEs” (GA No: 314024).

REFERENCES [1] Wu, D., Greer, M.J., Rosen, D.W., Schaefer, D., Cloud manufacturing: Strategic vision and state-of-the-art, Journal of Manufacturing Systems, Vol.32, No.4, pp.564-579, 2013. [2] Meier, H., Roy, R., Seliger, G., Industrial ProductService Systems—IPS2, CIRP Annals - Manufacturing Technology, Vol.59, No.2, pp.607-627, 2010. [3] IT Industry Innovation Council, Cloud Computing – Opportunities and Challenges, 2011, Available from: http://ict-industry-reports.com/wpcontent/uploads/sites/4/2013/09/2011-Cloud-ComputingOpportunities-Challenges-Australia-ICT-Innov-Council-Dec2011.pdf.

20

[4] Fernando, N., Loke, S.W., Rahayu, W., Mobile cloud computing: A survey, Future Generation Computer Systems, Vol.29, No.1, pp.84-106, 2013. [5] World Economic Forum. The Global Information Technology Report 2014: Rewards and Risks of Big Data, 2014 ,Available from: http://www3.weforum.org/docs/WEF_GlobalInformationTech nology_Report_2014.pdf. [6] World Economic Forum. Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, Geneva, 2015, Available from: http://www3.weforum.org/docs/WEFUSA_IndustrialInternet_ Report2015.pdf. [7] World Economic Forum. Exploring the Future of Cloud Computing: Riding the Next Wave of Technology-Driven Transformation, 2010, Available from: http://www.accenture.com/SiteCollectionDocuments/PDF/Acc enture_The_Future_of_Cloud_Computing.pdf. [8] Barbier, J., Bradley, J., Cooke, J., Handler, D., Lambrette, U., Popat, B., Stine, J., Uretsky, S., Riding the Cloud To Improve Your Top- and Bottom-Line Economics, Cisco Internet Business Solutions Group (IBSG), 2012, Available from: https://www.cisco.com/web/about/ac79/docs/CloudComputing-Economics_IBSG.pdf. [9] Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Byers A.H., Big Data: The next frontier for innovation, competition, and productivity,McKinsey Global Institute, 2011, Available from: http://www.mckinsey.com/insights/business_technology/big_d ata_the_next_frontier_for_innovation . [10] SAS. Big Data Meets Big Data Analytics,White Paper, 2012 ,Available from: http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/ big-data-meets-big-data-analytics-105777.pdf [11] Court D., Getting big impact from Big Data. McKinsey Quarterly, 2015, Available from: http://www.mckinsey.com/insights/business_technology/gettin g_big_impact_from_big_data . [12] Cloud Computing - Hearing with SMEs, EU report, 2011. [13] Zhang, L., Luo, Y., Tao, F., Li, B.H., Ren, L., Zhang, X., Guo, H., Cheng, Y., Anrui, H., Liu, Y., Cloud manufacturing: a new manufacturing paradigm, Enterprise Information Systems, Vol.8, No.2, pp. 167-187, 2014. [14] World Economic Forum, The Future of Manufacturing: Driving Capabilities, Enabling Investments, Geneva, 2014, Available from: http://www.cggc.duke.edu/pdfs/Future_of_Manufacturing_Dri ving_Capabilities.pdf . [15] Auschitzky E., Hammer M., Rajagopaul A., How Big Data can improve manufacturing, McKinsey & Company, 2014, Available from: http://www.mckinsey.com/insights/operations/how_big_data_ can_improve_manufacturing . [16] Wu, X., Qiao, F., Poon, K., Cloud manufacturing application in semiconductor industry, Winter Simulation Conference, Savannah, GA, 2014. [17] Wu, D., Rosen, D.W., Wang, L., Schaefer, D., Cloudbased Manufacturing: Old Wine in New Bottles?, Procedia CIRP, Vol.17, pp.94-99, 2014. [18] He, W., Xu, L., A state-of-the-art survey of cloud manufacturing, International Journal of Computer Integrated Manufacturing, Vol.28, No.3, pp.239-250, 2015. [19] Wang L., Gao R., Ragai I., An Integrated CyberPhysical System for Cloud Manufacturing. In ASME 2014

International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference, Detroit, 2014. [20] Scopus, Elsevier B.V., Available from: http://www.scopus.com/. [21] Mell P., Grance T., The NIST Definition of Cloud Computing, Gaithersburg, 2011, Available from : http://csrc.nist.gov/publications/nistpubs/800-145/SP800145.pdf . [22] Xu, X., From cloud computing to cloud manufacturing, Robotics and Computer-Integrated Manufacturing, Vol.28, No.1, pp.75-86, 2012. [23] Menasce, D., QoS issues in Web services, IEEE Internet Computing, Vol.6, No.6, pp.72-75, 2002. [24] Wu, D., Thames, J.L., Rosen, D.W., Schaefer, D., Enhancing the Product Realization Process With Cloud-Based Design and Manufacturing Systems, Journal of Computing and Information Science in Engineering, Vol.13, pp.041004, 2013. [25] Fatahi Valilai, O., Houshmand, M., A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm, Robotics and Computer-Integrated Manufacturing, Vol.29, No.1, pp.110-127, 2013. [26] Li, B.M., Xie, S.Q., Sang, Z.Q., Step-Based Data Sharing and Exchange in One-of-a-Kind Product Collaborative Design for Cloud Manufacturing, Advances in Mechanical Engineering, Vol.2013, pp.1-16, 2013. [27] Wu, D., Rosen, D.W., Wang, L., Schaefer, D., Cloudbased design and manufacturing: A new paradigm in digital manufacturing and design innovation, Computer-Aided Design, Vol.59, pp.1-14, 2015. [28] Wu, D., Rosen, D.W., Schaefer, D., Cloud-Based Design and Manufacturing: Status and Promise, Cloud-Based Design and Manufacturing, Berlin, Springer, pp. 1-24, 2014. [29] Liu, I., Jiang, H., Research on key technologies for design services collaboration in cloud manufacturing. In Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE, pp. 824-829, 2012. [30] Gao, Y.C., Feng, Y.X., Cheng, J., Tan, J., Hao, Z., Design Intend Solving: Dynamic Composition Method for Innovative Design Based on Virtual Cloud Manufacturing Resource Generators, Advances in Mechanical Engineering, Vol.2013, pp.1-10, 2013. [31] Taylor S.J, Kiss T., Terstyanszky G., Kacsuk P., Fantini N., Cloud computing for simulation in manufacturing and engineering: introducing the CloudSME simulation platform. In Proceedings of the 2014 Annual Simulation Symposium, Society for Computer Simulation International, pp. 12, 2014: [32] Laili Y., Zhang L., Tao F., Energy adaptive immune genetic algorithm for collaborative design task scheduling in cloud manufacturing system. In Industrial Engineering and Engineering Management (IEEM), IEEE International Conference, pp. 1912-1916,2011. [33] Paniti, I., Adaptation of Incremental Sheet Forming into cloud manufacturing, CIRP Journal of Manufacturing Science and Technology, Vol.7, No.3, pp.185-190, 2014. [34] Tapoglou N., Mehnen J., Doukas M., Mourtzis D., Optimal Machining Parameter Selection Based on Real-Time Machine Monitoring Using IEC 61499 Function Blocks for Use in a Cloud Manufacturing Environment: A Case Study for Face Milling. In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME, Detroit, 2014.

21

[35] Tapoglou, N., Mehnen, J., Vlachou, A., Doukas, M., Milas, N., Mourtzis, D., Cloud based platform for optimal machining parameter selection based on function blocks and real time monitoring, Journal of Manufacturing Science and Engineering, 2015, DOI:10.1115/1.4029806. [36] Wang, L., Machine availability monitoring and machining process planning towards Cloud manufacturing, CIRP Journal of Manufacturing Science and Technology, Vol.6, No.4, pp.263-273, 2013. [37] Park, J.H., Jeong, H.Y., Cloud computing-based jam management for a manufacturing system in a Green IT environment, The Journal of Supercomputing, Vol.69, No.3, pp.1054-1067, 2014. [38] Fatahi Valilai, O., Houshmand, M., A platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing paradigm, International Journal of Computer Integrated Manufacturing, Vol.27, No.11, pp.10311054, 2014. [39] Zhu, L., Zhao, Y., Wang, W., A Bilayer Resource Model for Cloud Manufacturing Services, Mathematical Problems in Engineering, Vol.2013, pp.1-10, 2013. [40] Liu, X., Li, Y., Wang, W., Wang, L., A Feature Based Method for Product-Oriented Representation to Manufacturing Resources in Cloud Manufacturing, In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME, Detroit, 2014. [41] Liu, N., Li, X., A Resource Virtualization Mechanism for Cloud Manufacturing Systems, Enterprise Interoperability, Springer, Berlin, pp. 46-59, 2012. [42] Wang, S., Chen, G., Kang, L., Li, Q., Information model of cloud manufacturing resource based on semantic web, International Journal of Digital Content Technology and its Applications, Vol.6, No.19, pp.339-346, 2012. [43] Cheng, Y., Tao, F., Liu, Y., Zhao, D., Zhang, L., Xu, L., Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol.227, No.12, pp.1901-1915. 2013. [44] Lartigau, J., Nie, L., Xu, X., Zhan, D., Mou, T., Scheduling Methodology for Production Services in Cloud Manufacturing, IEEE. pp. 34-39, 2012. [45] Um, J., Choi, Y.C., Stroud, I., Factory Planning System Considering Energy-efficient Process under Cloud Manufacturing, Procedia CIRP, Vol. 17, pp. 553-558, 2014. [46] Helo, P., Suorsa, M., Hao, Y., Anussornnitisarn, P., Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, Vol.65, No. 4, pp. 646-656, 2014. [47] Song, T., Liu, H., Wei, C., Zhang, C., Common engines of cloud manufacturing service platform for SMEs, The International Journal of Advanced Manufacturing Technology. Vol. 73, No. 1-4, pp. 557-569, 2014. [48] Huang, B., Li, C., Yin, C., Zhao, X., Cloud manufacturing service platform for small- and medium-sized enterprises. The International Journal of Advanced Manufacturing Technology, Vol. 65, No. 9-12, pp. 1261-1272, 2013. [49] Giriraj, M., Muthu, S., From Cloud Computing to Cloud Manufacturing Excution Assembly System, In Trends in Intelligent Robotics, Automation, and Manufacturing, Springer, pp. 303-312, 2012. [50] Wu, D., Rosen, D.W., Schaefer, D., Modeling and Analyzing the Material Flow of Crowdsourcing Processes in Cloud-Based Manufacturing Systems Using Stochastic Petri Nets, ASME 2014 International Manufacturing Science and

Engineering Conference collocated with the JSME, Detroit, 2014. [51] Wang, S.L., Guo, L., Kang, L., Li, C.S., Li, X.Y., Stephane, Y.M., Research on selection strategy of machining equipment in cloud manufacturing. The International Journal of Advanced Manufacturing Technology, Vol 71, No. 9-12, pp. 1549-1563, 2014. [52] Guo, Z.X., Wong, W.K., Guo, C., A cloud-based intelligent decision-making system for order tracking and allocation in apparel manufacturing. International Journal of Production Research. Vol. 52, No. 4, pp. 1100-1115, 2013. [53] Qu, T., Lei, S., Chen, Y., Wang, Z., Luo, H., Huang, G.Q., Internet-of-Things-Enabled Smart Production Logistics Execution System Based on Cloud Manufacturing, ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME, Detroit, 2014. [54] Tao, F., Zuo, Y., Xu, L.D., Zhang, L., IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing. IEEE Transactions on Industrial Informatics, Vol. 10, No.2, pp. 1547-1557, 2014. [55] Yang, X., Shi, G., Zhang, Z., Collaboration of large equipment complete service under cloud manufacturing mode, International Journal of Production Research, Vol. 52, No. 2, pp. 326-336, 2013. [56] Cheng, Y.Y., Shaw, H.J., Cloud-based, service-oriented and knowledge-sharing architecture: its design and application in shipbuilding, International Journal of Computer Integrated Manufacturing, Vol 28, No. 2, pp. 137-154, 2014. [57] Su, X., Gao, L., Wang, W., Wei, L., The Research on Service Platform of Ship Manufacturing in Cloud Manufacturing Environment, International Conference on eEducation, e-Business and Information Management (ICEEIM), Shanghai, China, 2014. [58] Chen, T., Strengthening the Competitiveness and Sustainability of a Semiconductor Manufacturer with Cloud Manufacturing, Sustainability,Vol. 6, No. 1, pp. 251-266, 2013. [59] Wang, L., Wang, X.V., Gao, L., Váncza, J., A cloudbased approach for WEEE remanufacturing, CIRP Annals Manufacturing Technology, Vol. 63, No. 1, pp. 409-412. 2014. [60] Qanbari, S., Zadeh, S.M., Vedaei, S., Dustdar, S., CloudMan: A platform for portable cloud manufacturing services, IEEE International Conference on Big Data, Washington, DC, 2014. [61] Ferreira, L., Putnik, G., Cruz-Cunha, M.M., Putnik, Z., Castro, H., Alves, C., Dashboard Services for PragmaticsBased Interoperability in Cloud and Ubiquitous Manufacturing. International Journal of Web Portals, Vol.6, No. 1, pp. 35-49, 2014. [62] Shang, X., Liu, X., Xiong, G., Cheng, C., Ma, Y., Nyberg, T.R., Social manufacturing cloud service platform for the mass customization in apparel industry, Service Operations and Logistics, and Informatics (SOLI), IEEE. pp. 220-224, 2013. [63] Cao, W., Jiang, P., Cloud Machining Community for Social Manufacturing. Applied Mechanics and Materials, Vol. 220-223, pp. 61-64, 2012 [64] Rauschecker, U., Meier, M., Muckenhirn, R., Yip, A.L.K., Jagadeesan, A.P., Corney, J., Cloud-based manufacturing-as-a-service environment for customized products, eChallenges e-2011, Florence, Italy, 2011. [65] Armstrong, D., Djemame K., Towards Quality of Service in the Cloud, 25th UK Performance Engineering Workshop, Leeds, 2009.

22

[66] Ardagna D., Casale G., Ciavotta M., Pérez J. F. and Wang W., Quality-of-service in cloud computing: modeling techniques and their applications, Journal of Internet Services and Applications, Vol. 5, Vo. 1, pp. 1-17, 2014. [67] Tao, F., Zhang, L., Venkatesh, V.C., Luo, Y., Cheng, Y., Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 225, No. 10, pp. 1969-1976, 2011. [68] Wang, V.X., Xu, X.W., An interoperable solution for Cloud manufacturing. Robotics and Computer-Integrated Manufacturing, Vol. 29, No. 4, pp. 232-247, 2013. [69] Casas P., Schatz R., Quality of Experience in Cloud services: Survey and measurements, Computer Networks, Vol. 68, pp. 149-165, 2014. [70] Lu, Y., Xu, X., Xu, J., Development of a Hybrid Manufacturing Cloud. Journal of Manufacturing Systems.; Vol. 33, No 4, pp. 551-566, 2014. [71] Tao, F., LaiLi, Y., Xu, L., Zhang, L., FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System, IEEE Transactions on Industrial Informatics, Vol.9, No.4, pp. 2023-2033, 2013. [72] Wang, T., Guo, S., Lee, C.G., Manufacturing task semantic modeling and description in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, Vol.71, No.9-12, pp. 2017-2031, 2014. [73] Lartigau, J., Xu, X., Nie, L., Zhan, D., Cloud manufacturing service composition based on QoS with geoperspective transportation using an improved Artificial Bee Colony optimisation algorithm. International Journal of Production Research, 2015. [74] Laili, Y., Tao, F., Zhang, L., Sarker, B.R., A study of optimal allocation of computing resources in cloud manufacturing systems. The International Journal of Advanced Manufacturing Technology,Vol. 63, No.(5-8), pp. 671690,2012. [75] Liu, W., Liu, B., Sun, D., Li, Y., Ma, G., Study on multitask oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems. International Journal of Computer Integrated Manufacturing, Vol. 26, No. 8, pp. 786-805,,2013. [76] Xiang, F., Hu, Y., Yu, Y., Wu, H., QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Central European Journal of Operations Research, Vo.22, No.4,pp. 663-685, 2014. [77] Tian, S., Liu, Q., Xu, W., Yan, J., A Discrete Hybrid Bees Algorithm for Service Aggregation Optimal Selection in Cloud Manufacturing, In Intelligent Data Engineering and Automated Learning – IDEAL, Berlin, Springer,pp. 110-117, 2013. [78] Jiao, H., Zhang, J., Li, J.H., Shi, J., Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S, International Journal of Computer Integrated Manufacturing, 2015. [79] Subashini, Kavitha, V., A survey on security issues in service delivery models of cloud computing, Journal of Network and Computer Applications, Vol. 34, No. 1, pp. 1-11, 2011. [80] Takabi, H., Joshi, J.B.D., Ahn, G.J., Security and Privacy Challenges in Cloud Computing Environments, Security & Privacy, Vol. 8, No. 6, pp. 24-31, 2010. [81] Rong, C., Nguyen, S.T., Jaatun, M.G., Beyond lightning: A survey on security challenges in cloud computing, Computers & Electrical Engineering, Vol. 39, No. 1, pp. 4754, 2013.

[82] Zissis, D., Lekkas, D., Addressing cloud computing security issues, Future Generation Computer Systems, Vol. 28, No.3, pp. 583-592, 2012. [83] Cloud Security Alliance, Top Threats to Cloud Computing V1.0, 2010, Available from:

https://cloudsecurityalliance.org/topthreats/csathreats.v1 .0.pdf . [84] Ali, M., Khan, S.U., Vasilakos, A.V., Security in cloud computing: Opportunities and challenges. International Journal of Computer Networks, Vol. 3, No.5, 2011. [85] Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., Zhao, X., Cloud manufacturing: from concept to practic, Enterprise Information Systems, Vol.9, No.2, pp. 186-209, 2015. [86] Lombardi, F., Di Pietro, R.. Secure virtualization for cloud computing, Journal of Network and Computer Applications, Vol. 34, No.4, pp. 1113-1122, 2013. [87] Li, J., Li, B., Wo, T., Hu, C., Huai, J., Liu, L., Lam, K.P., CyberGuarder: A virtualization security assurance architecture for green cloud computing, Future Generation Computer Systems, Vol. 28, No. 2, pp. 379-390, 2012. [88] Tao, F., Cheng, Y., Xu, L.D., Zhang, L., Li, B.H., CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System, IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 1435-1442, 2014. [89] Nelson, L.E., The Forrester Wave™: Enterprise Public Cloud Platforms, Cambridge, MA, 2014, Available from: https://d0.awsstatic.com/analystreports/The%20Forrester%20Wave%20Enterprise%20Public %20Cloud%20Platforms,%20Q4%202014.pdf. [90] Invensys, The Cloud for Manufacturing, Available from: http://global.wonderware.com/EN/PDF%20Library/WhitePap er_InvensysandMicrosoft.pdf. [91] IBM, Cloud Computing, Available from: http://www.ibm.com/cloud-computing/us/en/. [92] Google, Cloud Platform, Available from: https://cloud.google.com/customers/.

23

[93] PLEX, Cast Aluminum Solutions Case Study, 2014, Available from: http://www.plex.com/content/dam/plex/en_us/resources/casestudy/cast_aluminum_solutions_CS.pdf. [94] PLEX, Avon Gear Company Case Study, 2014, Available from: https://www.plex.com/content/dam/plex/en_us/resources/casestudy/Avon_Gear_CS.pdf. [95] Buyya, R., Pandey, S., Vecchiola, C., Cloudbus Toolkit for Market-Oriented Cloud Computing, Cloud Computing. Berlin, Springer, pp. 24-44, 2009. [96] AWS, Customer Stories, Available on: http://aws.amazon.com/solutions/case-studies/all/. [97] Li, Z., O'Brien, L., Cai, R., Zhang, H., Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services, IEEE Fifth International Conference on Cloud Computing, Honolulu, 2012. [98] Narcisi, G., Open source vs. proprietary cloud provider platforms, 2013, Available from: http://searchtelecom.techtarget.com/news/2240185995/Opensource-vs-proprietary-cloud-provider-platforms. [99] Finnegan, M., BMW chooses OpenStack for private cloud over commercial vendors due to lock-in risk, 2014, Available from: http://www.computerworlduk.com/indepth/cloud-computing/3584302/bmw-chooses-openstack-forprivate-cloud-over-commercial-vendors-due-to-lock-in-risk/. [100] Apache CloudStack Users, Available from: http://cloudstack.apache.org/users.html. [101] Simmon, E., Kyoung-Sook, K., Subrahmanian, E., Lee, R.,Vaulx, F., Murakami, Y., Zettsu, K., Sriram, R.D., A vision of Cyber-Physical Cloud Computing for Smart Networked Systems, NIST, 2013, Available from: http://www2.nict.go.jp/univ- com/isp/doc/NIST.IR.7951.pdf.