In recent years, building business applications from independently developed services has become one of the current trends in service computing. To satisfy clients’ requirements, service composition is performed to compose the various capabilities of available services. With the proliferation of services having similar functionalities, assessing the quality of a given composition is a paramount factor to decide which services must be selected, or to choose whether a given composition can provide the requested QoS (Quality of Service). However, due to the fluctuating conditions in the dynamic cloud computing environment, the QoS and the performances of the services become unreliable, and therefore call into the question the accuracy of the composition QoS. To tackle this issue, different methods that analyse the QoS have been developed, making it possible to help the designers to first, understand the system behaviour when providers and consumers, are interacting, thus allowing to optimize the system by identifying performance bottlenecks within a specified deployment environment.

In this study, the particular problem of many consumers that are competing to acquire services with same functionalities but with different QoS, is considered. For this purpose, assuming a dynamic environment, different models based on Discrete Time Markov Chain are developed to implement several policies of the system. A theoretical modelling of the problem is proposed including analytical results obtained using the Xborne tool. For each model, the average time to reach a given QoS for a community of consumers is reported. Such a performance metric allows the designer to predict the system behaviour in a dynamic environment.

近年来，从独立开发的服务构建业务应用程序已成为服务计算的当前趋势之一。为了满足客户的需求，执行服务组合以组合可用服务的各种功能。随着具有相似功能的服务的激增，评估给定组合的质量是决定必须选择哪些服务或选择给定组合是否可以提供请求的QoS（服务质量）的首要因素。然而，由于动态云计算环境中的波动条件，QoS和服务的性能变得不可靠，因此提出了组合QoS的准确性的问题。为了解决这个问题，已经开发了分析QoS的不同方法，

在这项研究中，考虑了许多争夺具有相同功能但具有不同QoS的服务的消费者的特殊问题。为此，在动态环境下，开发了基于离散时间马尔可夫链的不同模型来实现系统的多种策略。提出了该问题的理论模型，包括使用Xborne工具获得的分析结果。对于每种模型，报告了一个社区消费者达到给定QoS的平均时间。这种性能指标使设计人员可以预测动态环境中的系统行为。