To ensure high reliability and rapid error recovery in commercial core router systems, a health-status analyzer is essential to monitor the different features of core routers. However, traditional health analyzers need to store a large amount of historical data in order to identify health status. The storage requirement becomes prohibitively high when we attempt to carry out long-term health-status analysis for a large number of core routers. We describe the design of a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence to do health analysis. The symbolic aggregation approximation (SAX), 1d-SAX, moving-average-based trend approximation, and nonparametric symbolic approximation representation methods are implemented to encode complex time series in a hierarchical way. Hierarchical agglomerative clustering and sequitur rule discovery are implemented to learn important global and local patterns. Three classification methods including a vector-space-model-based approach are then utilized to identify the health status of core routers. Data collected from a set of commercial core router systems are used to validate the proposed health-status analyzer. The experimental results show that our symbol-based health status analyzer requires much lower storage than traditional methods, but can still maintain comparable diagnosis accuracy.

中文翻译：

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在核心路由器系统中使用时间序列数据进行基于分层符号的健康状态分析

为了确保商用核心路由器系统的高可靠性和快速错误恢复，健康状态分析器对于监控核心路由器的不同特性至关重要。然而，传统的健康分析仪需要存储大量的历史数据才能识别健康状况。当我们尝试对大量核心路由器进行长期健康状态分析时，存储要求变得非常高。我们描述了基于符号的健康状态分析器的设计，该分析器首先将从多个核心路由器收集的长期复杂时间序列编码为符号序列，然后利用符号序列进行健康分析。符号聚合近似 (SAX)，1d-SAX，基于移动平均的趋势近似，和非参数符号近似表示方法被实现以分层方式编码复杂的时间序列。实施分层凝聚聚类和序列规则发现以学习重要的全局和局部模式。然后利用包括基于向量空间模型的方法在内的三种分类方法来识别核心路由器的健康状态。从一组商业核心路由器系统收集的数据用于验证建议的健康状态分析器。实验结果表明，我们基于符号的健康状态分析器需要比传统方法低得多的存储空间，但仍能保持相当的诊断准确性。实施分层凝聚聚类和序列规则发现以学习重要的全局和局部模式。然后利用包括基于向量空间模型的方法在内的三种分类方法来识别核心路由器的健康状态。从一组商业核心路由器系统收集的数据用于验证建议的健康状态分析器。实验结果表明，我们基于符号的健康状态分析器需要比传统方法低得多的存储空间，但仍能保持相当的诊断准确性。实施分层凝聚聚类和序列规则发现以学习重要的全局和局部模式。然后利用包括基于向量空间模型的方法在内的三种分类方法来识别核心路由器的健康状态。从一组商业核心路由器系统收集的数据用于验证建议的健康状态分析器。实验结果表明，我们基于符号的健康状态分析器需要比传统方法低得多的存储空间，但仍能保持相当的诊断准确性。