A novel cloud manufacturing framework with auto-scaling capability for the machining industry

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Title:	A novel cloud manufacturing framework with auto-scaling capability for the machining industry
Authors:	Chen, CC (Chen, Chao-Chun) Lin, YC (Lin, Yu-Chuan) Hung, MH (Hung, Min-Hsiung) Lin, CY (Lin, Chih-Yin) Tsai, YJ (Tsai, Yen-Ju) Cheng, FT (Cheng, Fan-Tien)
Contributors:	資工系
Keywords:	e-manufacturing manufacturing information systems web-based manufacture web-based design advanced manufacturing technology systems design
Date:	2016
Issue Date:	2017-04-17 13:14:29 (UTC+8)
Abstract:	The globalised machine-tool manufacturing enterprises are eager to develop intelligent machine tools and novel business models to increase their competitiveness. In recent years, cloud manufacturing, encapsulating distributed manufacturing resources into cloud services for supporting all tasks in a product life cycle, has emerged as a promising concept and approach for the machining industry to achieve such a goal and gain profits. However, there has been no systematic approach to the development of cloud manufacturing systems (CMSs) for the machining industry so far. In this paper, we propose a novel cloud manufacturing framework (CMF) with auto-scaling capability (called CMFAS) aimed at providing a systematic and rapid development approach for building CMSs. The proposed CMFAS contains a cloud-based architecture which can transform single-user manufacturing functions (MFs) into cloud services that can be accessed by many users simultaneously. Also, a user-acceptable time-based scaling algorithm is designed so that the CMFAS can automatically perform scale-out or scale-in on the number of virtual machines (VMs) according to the user arrival rate, while confining the average service time for a user to be less than a specified user-acceptable time using a minimum number of VMs. Finally, we develop an Ontology Inference Cloud Service (OICS) for machine tools based on the CMFAS and deploy it on a public cloud platform for conducting integrated tests. Testing results show that the OICS can successfully recommend proper machine tools and cutting tools for machining tasks, and the proposed scaling algorithm outperforms traditional CPU-load-based scaling algorithms in terms of a smaller average service time for a user (i.e. quicker processing time) and a smaller number of created VMs (i.e. less cost of leasing cloud resources). The proposed CMFAS, together with its detailed designs, can serve as a useful reference approach for systematically and rapidly building CMSs for the machining industry.
Relation:	INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 卷: 29 期: 7 頁碼: 786-804
Appears in Collections:	[Department of Computer Science and Information Engineering] journal articles

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