Quantifying The Service Performance Impact Of Self-Organizing Network Actions

As smartphone users increasingly on cellular networks to access voice, video, and web applications, guaranteeing good performance and high availability is more important than ever. Historically, managing cellular network configuration has been a manual, error-prone process; recently, automated solutions such as SON (Self-Organizing Networks) controllers are being deployed for dynamic tuning of network configuration to improve end-user service performance under dynamic network and traffic conditions. SON automates many aspects of cellular network configuration, but it is nonetheless susceptible to software bugs and expected traffic changes that could result in sub-optimal performance. In this paper, we propose a capability (Veracity) to analyze and quantify the performance effects of SON actions. Assessing the effects of SON control is difficult because of the dynamic nature of SON and the dependency of end-user performance on factors such as radio channel quality, mobility and traffic load. Veracity addresses these using model driven impact detection and quantification. Our evaluation using data collected from an operational cellular network demonstrates that Veracity is accurate. Veracity is now being used by the service providers' field operation teams for the assessment of SON effectiveness in arenas and stadiums.