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A software defined-based hybrid cloud for the design of smart micro-manufacturing system

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Abstract

For purposes of re-configurability and flexibility, a few control data are extracted from manufacturing data generated from a production line in a smart factory. A production manager can rearrange those control data selected from the manufacturing data to formulate the mission of a production line. All related data are saved in a cloud storage after being verified by a security mechanism. They can be accessed only with permission. Since the structures of control data and manufacturing data are different, they are saved in various databases. However, accessing different databases results in additional communication cost, and the data-save performance will be decreased simultaneously. The production line setting may be changed based on the mission of the received orders, so the control data will be modified. It costs huge communication overhead if the cloud storage queries the control data for each request. In this paper, we propose two cache-based mechanisms, termed laziness approach and flow-based update (FBU) approach, to reduce the cost of verifying the save permission. The laziness approach gets the corresponding control data when the received data can not be matched to the information in the cache. The update process of the FBU approach is similar to that of the laziness, but the FBU downloads the entire control data of the specific production line. According to our analysis results, both mechanisms provide better performance than that of on-demand approach in terms of data-save process. In the worst case analysis, the FBU approach only needs a half cost of that required by the laziness approach. Moreover, the optimal cache size is inversely proportional to the stability of the production line setting, and we also suggest an optimal setting of the cache size.

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Notes

  1. ERP is a resource management system which can help manager to make the decision based on the current resources. ERP covers some business issues including financial management, production management, supply chain management, customer relationship management etc. For the perspective of manufacturing, ERP provides the order information and production line schedule.

  2. MES is a manufacturing management system which can capture requirements from orders, mointer production status, and control manufacturing flow. During manufacturing, the production manager can handle the production quality via MES.

References

  • Baccelli E, Mehlis C, Hahm O, Schmidt TC, Whlisch M (2014) Information centric networking in the IoT: experiments with NDN in the wild. In: Proceedings of the 1st International Conference on information-centric networking, pp 77–86

  • Bahga A, Madisetti VK, Madisetti RK, Dugenske A (2016) Software defined things in manufacturing networks. J Softw Eng Appl 9(09):425–438

    Article  Google Scholar 

  • Bernstein PA, Goodman N (1981) Concurrency control in distributed database systems. ACM Comput Surv 13(2):185–221

    Article  MathSciNet  Google Scholar 

  • Corbett JC, Dean J, Epstein M, Fikes A, Frost C, Furman JJ, Ghemawat S, Gubarev A, Heiser C, Hochschild P, Hsieh W, Kanthak S, Kogan E, Li H, Lloyd A, Melnik S, Mwaura D, Nagle D, Quinlan S, Rao R, Rolig L, Saito Y, Szymaniak M, Taylor C, Wang R, Woodford D (2013) Spanner: googles globally distributed database. ACM Trans Comput Syst 31(3):8

    Article  Google Scholar 

  • Do JD, Zhang D, Patel JM, DeWitt DJ, Naughton JF, Halverson A (2011) Turbocharging DBMS buffer pool using SSDs. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp 1113–1124

  • Hu J, Jiang H, Tian L, Xu L (2010) PUD-LRU: an erase-efficient write buffer management algorithm for flash memory SSD. IEEE International Symposium on modeling, analysis & simulation of computer and telecommunication systems (MASCOTS), pp 69–78

  • Ivanov D, Dolgui A, Sokolov B, Werner F, Ivanova M (2016) A, dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int J Prod Res 54(2):386–402

    Article  Google Scholar 

  • Jararweh Y, Al-Ayyoub M, Darabseh A, Benkhelifa E, Vouk M, Rindos A (2016) Software defined cloud: survey, system and evaluation. Future Gener Comput Syst 58:56–74

    Article  Google Scholar 

  • Kim H, Feamster N (2013) Improving network mnagement with software defined networking. IEEE Commun Mag 51(2):114–119

    Article  Google Scholar 

  • Lee J, Kao HA, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Proc CIRP 16:3–8

    Article  Google Scholar 

  • Li J, Deshpande A, Khuller S (2009) Minimizing communication cost in distributed multi-query processing. IEEE 25th International Conference on Data Engineering, pp 772–783

  • Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10

    Google Scholar 

  • Mehrabi MG, Ulsoy AG, Koren Y, Heytler P (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems. J Intell Manuf 13(2):135–146

    Article  Google Scholar 

  • Mookherjee D, Tsumagari M (2014) Mechanism design with communication constraints. J Polit Econ 122(5):1094–1129

    Article  Google Scholar 

  • Posada J, Toro C, Barandiaran I, Oyarzun D, Stricker D, Amicis R, Pinto EB, Eisert P, Döllner J, Vallarino I (2015) Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Comput Graph Appl 35(2):26–40

    Article  Google Scholar 

  • Raza MH, Sivakumar SC, Nafarieh A, Robertson B (2014) A comparison of software defined network (SDN) implementation strategies. Proc Comput Sci 32:1050–1055

    Article  Google Scholar 

  • Rmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M (2015) Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consulting Group, Boston, p 9

  • Rmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, Harnisch M (2017) Industry 4.0 framework for management and operations: a review. J Ambient Intell Humaniz Comput pp 1–13

  • Roblek V, Meko M, Krape AA (2016) Complex view of industry 4.0. SAGE Open 6(2):1–11

    Article  Google Scholar 

  • Stadtler H (2015) Supply chain management: an overview. In: Supply chain management and advanced planning, pp 3–28

  • Thames L, Schaefer D (2016) Software-defined cloud manufacturing for industry 4.0. Proc CIRP 52:12–17

    Article  Google Scholar 

  • Wan J, Yan H, Liu Q, Zhou K, Lu R, Li D (2013) Enabling cyberphysical systems with machinetomachine technologies. Int J Ad Hoc Ubiquitous Comput 13(3–4):187–196

    Article  Google Scholar 

  • Wan J, Tang S, Shu Z, Li D, Wang S, Imran M, Vasilakos AV (2016) Software-defined industrial internet of things in the context of industry 4.0. IEEE Sens J 16(20):7373–7380

    Article  Google Scholar 

  • Wang C, Vazhkudai SS, Ma X, Meng F, Kim Y, Engelmann C (2012) NVMalloc: exposing an aggregate SSD store as a memory partition in extreme-scale machines. IEEE 26th International parallel & distributed processing Symposium (IPDPS), pp 957–968

  • Wettschereck D, Aha DW, Mohri T (1997) A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Lazy learning. Springer Netherlands, Dordrecht, pp 273–314

    Chapter  Google Scholar 

  • Xu LD, He W, Li S (2014) Internet of things in industries: a survey. IEEE Trans Ind Inf 10(4):2233–2243

    Article  Google Scholar 

  • Zhang Y, Qiu M, Tsai CW, Hassan MM, Alamri A (2017) Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1):88–95

    Article  Google Scholar 

  • Zhang M-L, Zhou Z-H (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 41170, Taiwan (R.O.C.)

    Chen-Kun Tsung

  2. Institute of Industrial Engineering, National Taiwan University, Room 517, College of engineering building, No.30, Sec. 3, Xinhai Rd., Daan Dist., Taipei, 106, Taiwan (R.O.C.)

    Chun-Tai Yen & Wen-Fang Wu

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  1. Chen-Kun Tsung
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  2. Chun-Tai Yen
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  3. Wen-Fang Wu
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Correspondence to Chen-Kun Tsung.

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Tsung, CK., Yen, CT. & Wu, WF. A software defined-based hybrid cloud for the design of smart micro-manufacturing system. Microsyst Technol 24, 4329–4340 (2018). https://doi.org/10.1007/s00542-018-3779-4

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