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Cloud service selection based on QoS-aware logistics

  • Wenxue Ran
  • Huijuan LiuEmail author
Methodologies and Application

Abstract

With the development of technologies such as cloud computing, Big Data, the Internet of Things, etc., Internet + logistics models are being sought by all parties, leading to the current rise of cloud service platforms for logistics. As such platforms combine many logistics services with similar functions, identification of methods to choose from among a large number of similar services to meet the personalized needs of customers has become especially important. In the work presented herein, QoS data are quantified and filtered through the establishment of quality of service (QoS) decision information systems. Meanwhile, using the variable precision rough set method, the weight of each QoS attribute index is calculated, then the similarity of services, to obtain a comprehensive sequence in terms of service quality that provides a basis for selection of the optimal service. The calculation and analysis results show that this method can effectively choose the best logistics service according to specific business needs.

Keywords

Cloud computing Logistics cloud services QoS Service selection 

Notes

Acknowledgements

Funding was provided by National Natural Science Foundation Council of China (grant no. 71661029), Applied Basic Research Science Foundation of Yunnan Provincial Department of Science and Technology (grant no. 2015FD028), and Science Foundation of Yunnan Provincial Department of Education (grant no. 2015Y269).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

References

  1. Anji Y, Wang Z, Liu Z, Xue X (2014) Research on optimal composition of logistics web services based on domain QoS perception. Comput Sci 41(10):252Google Scholar
  2. Bessis N, Sotiriadis S, Pop F, Cristea V (2013) Using a novel message-exchanging optimization (MEO) model to reduce energy consumption in distributed systems. Simul Model Pract Theory 39:104–120CrossRefGoogle Scholar
  3. Chen X (2011) Web service QoS model of research. Nanjing University of Posts and Telecommunications, NanjingGoogle Scholar
  4. Cheng L (2018) Resource management system for big data cloud platform. Institute of Electronic Science, China Electronic Technology GroupGoogle Scholar
  5. Han M, Duan Y (2019) QoS optimization of web service composition based on credibility evaluation. Control Decis 9:23.  https://doi.org/10.13195/j.kzyjc.2019.0006 Google Scholar
  6. Kasap N, Turan, Hasan H et al (2016) Provider selection and task allocation in telecommunications with QoS degradation policy. Ann Oper Res 263:311–337Google Scholar
  7. Khanouche ME, Amirat Y, Chibani A et al (2016) Energy-centered and QoS-aware services selection for internet of things. IEEE Trans Autom Sci Eng 13:1256–1269CrossRefGoogle Scholar
  8. Lee KC, Jeon JH, Lee WS et al (2003) QoS for web services: requirement and possible approaches. W3C Work Group Note 25:1–9CrossRefGoogle Scholar
  9. Lin Y, Tian SH (2012) Logistics cloud service—a new supply chain oriented logistics service model. Comput Appl Res 29(1):224–228Google Scholar
  10. Liu J, Sun J, Jiang L (2010) A QoS evaluation model for cloud computing. Comput Knowl Technol 31:8801–8803Google Scholar
  11. Liu X, Kale A, Wasani J, Ding C, Yu Q (2015) Extracting, ranking, and evaluating quality features of web services through user review sentiment analysis. In: IEEE international conference on web services, pp 153–160Google Scholar
  12. Sfrent A, Pop F (2015) Asymptotic scheduling for many task computing in big data platforms. Inf Sci 319:71–91MathSciNetCrossRefGoogle Scholar
  13. Sîrbu A , Pop C et al (2016) Predicting provisioning and booting times in a Metal-as-a-service system. Future Gener Comput Syst 72:180–192Google Scholar
  14. Sun Q, Fu L, Pei X, Sun J (2017) Reliability improvement method of resource operation and maintenance management based on distributed system. Comput Appl 37(S1):243–245Google Scholar
  15. Vasile M-A, Pop F, Tutueanu R-I, Cristea V, Kołodziej J (2015) Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener Comput Syst 51:61–71CrossRefGoogle Scholar
  16. Wei L, Zhao Q, Shu H (2013) Cloud manufacturing environment based on trust evaluation of cloud services selection. J Comput Appl 33:23–27Google Scholar
  17. Wen P (2018) Thousands of benefits Research on task scheduling of logistics cloud service platform based on genetic algorithm. Logist Technol 41(04):5–9Google Scholar
  18. Yan H, Fu X, Yue K, Liu L, Liu L (2019) Using the uncertainty of prospect theory, QoS perceives Web service selection. Minicomput Syst 40(05):953–958Google Scholar
  19. Zhao J, Wang X (2016) Based on the service quality of cloud manufacturing services bidirectional matching model. J Comput Integr Manuf Syst 1(22):104–122Google Scholar
  20. Zhou Q, Wu H, Yue K, Hsu C-H (2019) Spatio-temporal context-aware collaborative QoS prediction. Future Gener Comput Syst 100:46–57CrossRefGoogle Scholar
  21. Zhu L (2014) Cloud manufacturing environment resource modeling and matching method research. Zhejiang University of Technology, ZhejiangGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Yunnan University of Finance and EconomicsKunmingChina
  2. 2.School of ManagementHenan Institute of TechnologyXinxiangChina

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