一种基于广义随机Petri网的Web服务组合性能预测模型

Web服务交互消息受到不可预知的网络环境的影响，因特网环境中的个体Web服务的性能表现与访问者的地域位置有关。于是，服务组合的性能则更加容易受到网络环境因素的影响。同时，服务组合的性能还会受到服务组合流程中不同的分支执行概率的影响。在这种情况下，服务组合建模只考虑组合本身，而不考虑网络环境因素和分支执行概率，这将会对模型的预测结果造成较大的偏差。为了能够真实地评估服务交互行为、网络环境与分支执行概率之间的关系，提出了一种基于广义随机Petri网的Web服务组合性能预测模型，该模型不仅引入了伙伴服务性能方面的因素，而且充分考虑了网络环境因素和分支执行概率，从而能够对被建模系统进行定性和定量分析，进而指导服务组合性能优化。

GSPN Based Web Service Composition Performance Prediction Model

The performance of individual Web service is related to the users' locations,because service interaction messages can be easily influenced by Internet environment. As a result, service composition, which is implemented by coordinating interactions between different Web services, is also greatly impacted by Internet However, current modeling methods of service composition focus on composition itself without the impact of Internet and branch execution probability. In order to describe the relationship between service interactions, Internet environment and branch execution probability, this paper introduced a Web services performance prediction model based on generalized stochastic Petri nets. The model takes not only the situations of individual Web services but also the factors of Internet environment and branch execution probability into considerations. The model is used to evaluate and optimize the performance of service compositions.