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Web服务质量属性评估技术研究

Research of WEB Service Quality Evaluation

【作者】 周成

【导师】 沈备军; 陈昊鹏;

【作者基本信息】 上海交通大学 , 软件工程, 2010, 硕士

【摘要】 服务质量(QoS)是服务组合和推荐时的关键因素,然而现阶段,大多数对服务质量的关注集中在静态服务质量及如何拓展服务描述语言,对其进行描述上。对于如何获取动态服务质量,对服务的质量进行度量等研究工作较少。这些工作对服务的扩展和服务质量属性的延伸方面都具有相当明显的重要性。所以需要一种动态收集质量属性的机制,由此来解决静态的服务质量不能实时反映Web服务的即时运行状态的问题。同时,注册中心查询服务质量的结果来源于注册中心所存储的QoS信息,因此把真实有效的QoS数据反馈给服务注册中心是相当必要的。本文以863专题课题“互联网环境下基于闭环反馈的服务描述、发现和管理技术研究”(课题编号2007AA01Z139)为研究背景。在本文中综合分析了三种收集动态QoS信息的方法,分别是基于面向方面编程技术的方法,基于代理的方法,和基于端口拦截的方法。在具体应用实现过程中,分别讨论了这些方法的优缺点,这些方法具有各自的应用环境,通过对它们的综合应用,使得我们反馈的适应性大大增强。本文采用OWL作为反馈消息的组织形式,作为一种丰富的,可扩展的本体语言,使得反馈的质量信息更加便于机器自动化识别处理。本文还提出了一个基于采样统计和动态反馈队列的QoS反馈模型。通过考虑错误处理和采样反馈,使得反馈过程可以节省大量客户端的硬件资源,并且提高了反馈数据的准确程度。通过具体实验的比较我们可以看到该模型在反馈数据的时候不仅仅对于质量变动可以敏感感知,同时确保了扰动数据对于结果稳定的影响。最后本文还介绍了,结合上述理论的一个Web服务质量监控客户端(Q-Spy)的设计实现过程。该工具的实现,增加客户端向服务注册中心进行反馈的协作。增加这个协作是因为我们发现大多数情况下,客户端有关服务质量的反馈在通过客户相似度分析等方法进行筛选后,相对于服务提供者反馈的数据而言,对于服务消费者按照服务质量进行服务查找来说更具有参考意义。

【Abstract】 Quality of Service (QoS) is an important factor during service composition and recommendation. However, at current stage, most of the researches of QoS focus on the static quality of services and the description of QoS through expanding WSDL. Little attention was paid in the fields of how to obtain the dynamic quality of services and the metrics of QoS, which are also obviously important in the expansion of services and the extension of service quality attributes. Since static QoS data cannot reflect the real-time performance of Web Service (WS), a mechanism is needed to collect the dynamic QoS data. Also, QoS query result is determined by the QoS data stored by service registry. Consequently, it is also necessary to use an efficient and accurate feedback method to calculate and send those dynamic QoS data to the service registry.This paper is supported by 863 program under Grant No. 2007AA01Z139, Research on service description, selection and evaluation based on closed-loop feedback mechanism under Internet environment. In this paper, we propose three methods to collect the real-time QoS data, namely AOP-based, Proxy-based, and Port-based. The application of the three methods will be discussed by comparing their advantages and disadvantages. In the implementation of specific application, we discussed the advantages and disadvantages of these methods. These methods have their own application environment, through their integrated applications, allows us to greatly enhance the adaptability of feedback. Since the feedback conforms to the OWL language, which is a rich and extensible modular ontology language, programs can understand the semantic feedback automatically by parsing the feedback messages.And we propose a QoS feedback model based on objective QoS metrics using some simple statistical theories and a dynamic queue as a data pool to cache all runtime status. Moreover, error determination and sampling feedback have been taken into consideration so that service provider assigns less hardware resource and avoids disturbing feedback result from unfriendly exception. By carrying out experiments, it demonstrates that this feedback model evaluates the WS performance better than other common methods. This model provides QoS metrics that are easy to rank and sensitive to the status change.Finally, we introduce software based on our theory. We call it Q-Spy. Implementation of the tool increases collaboration between client and the service registry through feedback. Add this collaboration because we found that in most cases, the client feedback on the quality of service through customer similarity analysis method, is much better than the service provider feedback data, for consumers to find more suitable services through the quality of service.

【关键词】 服务质量质量反馈服务发现组合服务
【Key words】 QoSFeedbackService DiscoveryComposite Service
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