基于分层混合专家神经网络的Web服务失效检测机制

Inspection Mechanism for Web Services Unavailability Based on Hierarchical Mixtures of Expert Neural Network

利用反射中间件来检测导致服务失效的各种状态和参数,从服务内部动态调整服务运行状态和配置,可以有效地避免服务失效.将分层混合专家神经网络(Hierarchical Mixtures of Expert neural network,HME)配置在反射中间件的元层中,用来检测这些服务的失效环境状态,并解决引起Web服务失效的状态.利用极大似然(Expectation-Maximization,EM)的学习策略对分层混合专家网络进行训练.实验和数据分析表明,HME网络作为反射中间件检测技术可以高效地对服务失效进行检测和辨识.

Reflective middleware is used to detect parameters and states of Web services which cause services unavailability, and the running state and configuration of services are dynamically adjusted internally to avoid Web services unavailability. The HME （ Hierarchical Mixtures of Expert neural network） is deployed to the meta-layer of the reflective middleware to detect and deal with the states and parameters which lead Web services unavailability. An expectation-maximization policy is presented as a learning strategy to train the HME. Experiment and analysis are made, and the results show that HME network as a detector in reflective middleware is highly efficient to detect services unavailability.