在云计算和服务计算的众多研究方向中，可信云服务一直是热点和难点。现有的相关工作重在形式化建模和性质验证，量化研究较少，在云资源调度管理策略量化分析、云服务组合细粒度控制流建模、运行时可信性趋势预测等方面存在诸多不足。本项目拟综合运用Petri网、随机过程与排队网络、协同过滤、时间序列分析等理论与方法，对云上Web服务可信性进行量化分析和预测。具体内容包括：1）运用随机Petri网对的云上Web服务组合进行细粒度定量建模，在不依赖状态分析前提下，提出基于结构等效约简的多指标可信性计算方法；2）对云系统进程迁移、动态速率拓展、主动重生、虚拟机-物理机动态映射等调度管理机制和系统行为进行随机建模，分析其对可信性的量化影响；3）对云服务和服务组合可信性历史数据进行时间序列建模和趋势预测；4）运用协同过滤方法对云服务缺失可信性数据进行分析。研究还将采集真实系统数据，对所提出的相关方法进行检验。

With the rapid development of cloud services and their success in electronic commerce, electronic government, and enterprise business-process-management, theoretical and experimental studies of trustworthiness are receiving increasing attention from both the industry and academy. Most existing studies on trustworthy cloud services concern the formalization and property-verification issues. However, quantitative and nonfunctional studies are still preliminary and limited. Modeling and prediction issues of cloud service composition control flows, trend analysis of run-time trustworthiness, cloud resource management are less considered. Targeting at the above-mentioned observations, this project presents a model-driven framework to quantitative study of trustworthy cloud services. Detailed topics are: (1) conducting quantitative modeling of cloud-based service compositions using Petri nets and introducing non-state-based methods to quantitative analysis of QoS/trustworthiness of composite cloud services; (2) modeling cloud management and scheduling policies (e.g., dynamic VM migration, PM speed scaling, proactive rejuvenation) and studying their influences on system trustworthiness/QoS; (3)employing time-series-based approaches to model the historical QoS data of cloud services and predict future QoS;(4) employing collaborative-filtering-based methods to analyze missing trustworthiness/QoS data of cloud services. The study also includes case studies based on real-world cloud services, where experimental QoS/trustworthiness data are used to validate the correctness and accuracy of proposed methods.