面向服务的系统的自修复质量故障预测

Hongbing Wang, C. Wan
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引用次数: 2

摘要

随着软件系统越来越多地应用于更加复杂和关键的环境中,面向服务的系统作为一种新型的软件系统结构受到越来越多的关注。在松耦合系统动态不确定的运行环境下,自愈过程作为系统运行的重要保障机制,对系统质量分析构成了很大的威胁。特别是,作为自我修复的第一步,质量失效预测研究不仅面临着对服务质量的持续而直接的干扰,而且还面临着用户对质量的复杂偏好。本文提出了一种基于随机微分方程的模型,以便更精确、更动态地分析扰动。采用加权条件偏好来考虑不同用户的需求。以中国某电信公司的系统平台实时采集的真实数据集为例,对该模型进行了验证。实验验证了模型的预测能力,并评估了参数对预测精度的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality Failure Prediction for the Self-Healing of Service-Oriented System of Systems
With software systems increasingly being employed in more complex and critical contexts, service-oriented system of systems has been paid more and more attention as a novel software system structure, which considers System as a Service. Under the loosely coupled SoS's dynamic and uncertain running environment, self-healing process, as the important safeguard mechanism of system running, pose a great threat to system quality analysis. Particularly, as the first step of self-healing, the research of quality failure prediction faces not only continual and immediate disturbance on service quality, but also complex users' preference on quality. In this paper, we propose a model based on Stochastic Differential Equations to analyze the disturbance more precisely and dynamically. And we adopt weighted conditional preference to consider different users' requirements. This model is testified in an empirical case study, in which the real data set is collected in real-time from the system platform of a Telecom in China. The experiments verify model's prediction abilities and evaluate the impact of the parameters on the prediction accuracy.
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