Cloud service selection based on QoS-aware logistics
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.
KeywordsCloud computing Logistics cloud services QoS Service selection
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.
- 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
- Chen X (2011) Web service QoS model of research. Nanjing University of Posts and Telecommunications, NanjingGoogle Scholar
- Cheng L (2018) Resource management system for big data cloud platform. Institute of Electronic Science, China Electronic Technology GroupGoogle Scholar
- 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
- Lin Y, Tian SH (2012) Logistics cloud service—a new supply chain oriented logistics service model. Comput Appl Res 29(1):224–228Google Scholar
- Liu J, Sun J, Jiang L (2010) A QoS evaluation model for cloud computing. Comput Knowl Technol 31:8801–8803Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zhu L (2014) Cloud manufacturing environment resource modeling and matching method research. Zhejiang University of Technology, ZhejiangGoogle Scholar