面向食品溯源数据服务的多QoS约束服务选择优化算法研究

摘要 数据服务的理念是大数据时代一种重要的信息服务方式.在面向服务构架（Service-Oriented Architecture，SOA）框架下研究了用于食品安全溯源系统中面向数据服务的多QoS（Quality of Service）约束下服务组合选择优化算法.溯源服务系统是一种数据密集型服务系统，系统的实现需要组合多源的Web服务.通过提取系统的重要Web服务和数据服务QoS指标，建立了食品安全溯源系统的以数据服务为场景的多服务属性约束下优化因子模型—MQBR（Multi-QoS based Benefit Ratio）模型，采用Skyline算法预处理并结合人工智能的启发式方法求解了满足约束条件的服务组合，提升了系统服务选择的效率和质量.通过实验分析证明了算法的有效性，且实现了多QoS约束条件下食品安全溯源系统中服务组合选择算法，提升了服务选择的质量和性能.
Abstract：The concept of data services plays an important role in the era of big data. In this paper, an optimization algorithm for web services in food safety traceability is investigated based on the SOA (Service-Oriented Architecture) framework. Traceability services are commonly data-intensive systems, which need to combine multi-source web services. In this paper, by extracting important QoS (Quality of Service) indexes from the web and data services of the system, a multi-QoS based benefit ratio (MQBR) is established, which is then used on the traceability platform for food security management. Based on the MQBR model, the skyline and heuristic method of artificial intelligence is proposed for optimizing the efficiency and quality of service selection. Experiments are conducted to prove the validity of the algorithm. The methods of our study are designed and applied to a food security management application, with multiple QoS constraints in the traceability systems, to improve the overall performance and service quality.