基于贝叶斯网络的服务质量优化方法

Lian Gaofeng
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引用次数: 4

摘要

视频会议作为互联网流媒体的一种应用,已经引起了学术界和产业界的广泛关注。然而,用户在日常使用中可能会遇到许多问题,例如视频质量差,播放延迟,缺乏可调节的上下文,这些都会对客户的使用体验产生负面影响。现有的端到端服务质量保证方法主要以“单一”的方式分析目标服务质量参数与上下文之间的关系。本文提出了一种基于贝叶斯网络的服务质量保证方法(称为综合上下文感知方法,CCA),该方法将贝叶斯网络与模糊集理论相结合,通过上下文感知获得不同服务质量参数之间的随机关系。综合实验清楚地验证了CCA相对于其他成熟方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bayesian NetworkBased MethodforService Quality Optimization
Video conference, as an application of Internet streaming media, has attracted wide attention from both academic and industrial sectors. However, usersmay encounter many problemsindailyuse, such as poor video quality, playback delay, and lack of adjustable context, whichcausenegative impactson customers’usage experience. Existing end-to-end service quality assurance method mainly analyzes the relationship between the target service quality parameters and the context in a “single” manner. In this paper, we propose a Bayesian network-based service quality assurance method (named as Comprehensively Context-Aware approach, CCA), which combines Bayesian network and fuzzy set theoryand obtainsrandomrelationshipsamongdifferent service quality parameters through contextual awareness. Comprehensive experimentsclearly validate the superiority of CCA against other well-established methods.
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