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.