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首页> 外文会议>IEEE Symposium on Reliable Distributed Systems >Call Availability Prediction in a Telecommunication System: A Data Driven Empirical Approach
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Call Availability Prediction in a Telecommunication System: A Data Driven Empirical Approach

机译：在电信系统中呼叫可用性预测：数据驱动的经验方法

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Availability prediction in a telecommunication system plays a crucial role in its management, either by alerting the operator to potential failures or by proactively initiating preventive measures. In this paper, we apply linear (ARMA, multivariate, random walk) and nonlinear (Radial and Universal Basis Functions) regression techniques to recognize system failures and to predict the system's call availability up to 15 minutes in advance. Secondly we introduce a novel nonlinear modeling technique for call availability prediction. We benchmark all five techniques against each other. The applied modeling methods are data driven rather than analytical and can handle large amounts of data. We apply the modeling techniques to real data of a commercial telecommunication platform. The data used for modeling includes a) time stamped event-based log files and b) continuously measured system states. Results are given in terms of a) receiver operator characteristics (AUC) for classification into classes of failure and non-failure states and b) as a cost-benefit analysis. Our findings suggest a) high degree of nonlinearity in the data, b) statistically significant improved forecasting performance and cost-benefit ratio of nonlinear modeling techniques, and finally finding that c) log file data does not contribute to improve model performance with any modeling technique.
机译：电信系统中的可用性预测在其管理中发挥着至关重要的作用，可以通过警告操作员或通过主动启动预防措施来提醒操作员。在本文中，我们应用线性（ARMA，多变量，随机步行）和非线性（径向和通用基本函数）回归技术来识别系统故障，并预先预测系统的呼叫可用性最多15分钟。其次，我们介绍了一种用于呼叫可用性预测的新型非线性建模技术。我们将所有五种技术互相结合。应用的建模方法是数据驱动而不是分析，可以处理大量数据。我们将建模技术应用于商业电信平台的实际数据。用于建模的数据包括a）时间戳的基于事件的日志文件和b）连续测量的系统状态。结果是通过A）接收器操作特征（AUC）的分类，以分类为失败和非故障状态，B）作为成本效益分析。我们的研究结果表明了数据，B）高度非线性，B）非线性建模技术的统计上显着提高的预测性能和成本效益比，而且最终发现C）日志文件数据没有有助于提高任何建模技术的模型性能。

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