Quantifying the Resilience of Cloud-Based Manufacturing Composite Services

Quantifying the Resilience of Cloud-Based Manufacturing Composite Services

Mohammad Reza Namjoo (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran), Abbas Keramati (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran & Ted Rogers School of Information Technology Management (TRSITM), Ryerson University, Toronto, Canada), S. Ali Torabi (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran) and Fariborz Jolai (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)
Copyright: © 2018 |Pages: 30
DOI: 10.4018/IJCAC.2018100106
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Composite services are regarded as essential components of a cloud-based manufacturing (CM) system. Different classes of disruptive events which are rooted in the uncertainties of the supply and demand sides of CM threaten the resilience and continuity of the composite services over time. The present article proposes a resilience measure based on a generic system resilience metric along with a two-step method based on MCDM and SIS-Monte-Carlo to quantify the resilience of CM composite services by considering recovery time objective (RTO). To present the applicability of the proposed method, a numerical example was designed and simulated. The results showed that the resilience of composite service is sensitive to geographical distance, RTO, and quality of recovery.
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1. Introduction

Cloud-based Manufacturing (CM) is a new production paradigm for the intelligent networking of geo-distributed manufacturing capabilities and resources. With the aid of the Internet, CM users can request for any kind of manufacturing services (e.g. design, maintenance, and construction services) as a cloud service (F. Tao, Zhang, Venkatesh, Luo, & Cheng, 2011). Different advanced technologies, such as the internet of things (IoT), cloud computing, virtualization, and service-oriented architecture (SOA) has enabled CM to create temporary and flexible production lines based on the users’ requirements and a combination of distributed and heterogeneous virtual resources (Wu, Greer, Rosen, & Schaefer, 2013).

Although Cloud-based manufacturing offers many benefits to its stakeholders, such as enhancing the efficiency of resource loading (Wu et al., 2013), improving collaboration(X. Xu, 2012), cooperation (T. Li, Gupta, & Metere, 2017), and increasing the intelligence of the system through big data analysis (Psannis, Stergiou, & Gupta, 2018); it also faces various kinds of incidental and intentional threats(Jouini & Rabai, 2016) and changes as a result of the complexity and dynamism of its operating environment and the heterogeneity of resources (Wu, Rosen, & Schaefer, 2014; W. Xu et al., 2012). Accordingly, security (Stergiou, Psannis, Kim, & Gupta, 2018), reliability, and safety (Wu et al., 2014) of the Cloud-based system are regarded as critical issues.

Service composition is a key and complex process of CM (Loskyll et al., 2011; F. Tao et al., 2011; Xiang, Hu, Yu, & Wu, 2014), whose results should be implemented in a completely uncertain and dynamic environment in the presence of a variety of risks (Fei Tao, LaiLi, Xu, & Zhang, 2013a) such as changes in job requirement, cancelation of submitted jobs, variations in quality of service (QoS) attributes, service disruption, entry of new services, and dependencies between services (L. Zhang, Guo, Tao, Luo, & Si, 2010).Therefore, the resilience of composite services at runtime to the disruptive internal and external factors is considered as an important property of the service.

In recent years, although resilience engineering in various areas of management and engineering has attracted much attention, however, the knowledge of analysing, designing and managing resilience is in the early stages (WJ Zhang & Van Luttervelt, 2011). The results of the authors’ review of research papers published between 2010 and 2018 on the service composition in Cloud-based manufacturing revealed that the focus of the researches has been on QoS-aware service composition, mainly considering overall time, cost, energy, reliability, availability, trust, reputation, security, and maintainability of the services (Chen, Dou, Li, & Wu, 2016; Huang, Li, & Tao, 2014; Jin, Yao, & Chen, 2017; Li, Guan, Liu, Ma & Zhang, 2018; Li, Jiang & Ge, 2014; Liu & Zhang, 2017; Lu & Xu, 2017; Que, Zhong, Chen, Chen & Ji, 2018; Tao, Li, Xu & Zhang, 2013b; Xu et al., 2017; Yongxiang, Xifan, Jie & Bin, 2013; Zhang, Yang, Zhang, Yu & Chen, 2017; Zhang, Yang, Zhang, Yu & Li, 2017; Zhou & Yao, 2017a, 2017b, 2017c, 2017d, 2017e). The QoS-aware composition is a multi-objective NP-hard optimization problem which seeks the best execution paths, consisting of several atomic manufacturing related Cloud services.

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