基于最大熵原理的Web服务QoS概率分布获取

为了进行服务风险管理,需要了解服务质量（QoS）的随机特性,而描述QoS随机特性的一种有效手段是获得其准确的概率分布。为此,提出了一种基于最大熵原理在小样本情况下获取Web服务QoS概率分布的方法。方法采用最大熵原理将小样本情况下QoS概率分布获取的问题规约为一个由已知QoS数据确定约束条件的最优化问题进行求解,获得QoS概率密度函数的解析式,然后设计了对该概率密度函数解析式参数进行估计的算法。最后,以实际的Web服务QoS数据为基础,通过实验验证了该方法对不同QoS分布获取时的有效性和合理性,并验证了分布获取算法的效率和终止性。

Probability distribution estimation for Web service QoS based on max entropy principle

To manage the risk of service,it is necessary to obtain stochastic character of Quality of Service(QoS) that is represented as accurate probability distribution.This paper presented an approach to estimate probability distribution of Web service QoS in the case of small number of samples.Using max entropy principle,the analytical formula of the probability density function can be obtained by transforming the probability distribution estimation problem into an optimal problem with constraints obtained from sampling QoS data.Then an algorithm to estimate parameters of the probability density function was designed.The experimental and simulation results based on real Web service QoS data show the effectiveness of the proposed approach for probability distribution estimation of different QoS attribute.The efficiency and feasibility of the distribution estimation algorithm have got validated by experiments too.