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.

The health status of core router systems needs to be analyzed efficiently in order to ensure high reliability and timely error recovery. Although a large amount operational data is collected from core routers, due to high computational complexity and expensive labor cost, only a small part of this data is labeled by experts. The lack of labels is an impediment toward the adoption of supervised learning. We present an iterative self-learning procedure for assessing the health status of a core router. This procedure first computes a representative feature matrix to capture different characteristics of time-series data. Not only statistical-modeling-based features are computed from three general categories but also a recurrent neural network-based autoencoder is utilized to capture a wider range of hidden patterns. Moreover, both minimum-redundancy-maximum-relevance (mRMR) method and fully connected feedforward autoencoder are applied to further reduce dimensionality of extracted feature matrix. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field data collected from a set of commercial core routers are used to experimentally validate the proposed health-status analyzer. The experimental results show that the proposed feature-based self-learning health analyzer achieves higher precision and recall than the traditional supervised health analyzer as well the currently deployed rule-based health analyzer. Moreover, it achieves better performance than the three anomaly detection baseline methods under the transformed binary classification scenario.

Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the statistical properties of the monitored data change significantly as time proceeds. We describe the design of a changepoint (CP)-based anomaly detector that first detects CPs from collected time-series data, and then utilizes these CPs to detect anomalies. Different CP detection approaches are implemented to detect various types of CPs. A clustering method is then developed to identify normal/abnormal patterns from CP windows. Data collected from a set of commercial core router systems are used to validate the proposed anomaly detector. Experimental results show that our CP-based anomaly detector achieves better performance than traditional methods in terms of two metrics, namely success ratio and nonfalse-alarm ratio.

Data-driven prognostic health management is essential to ensure high reliability and rapid error recovery in commercial core router systems. The effectiveness of prognostic health management depends on whether failures can be accurately predicted with sufficient lead time. This paper describes how time-series analysis and machine-learning techniques can be used to detect anomalies and predict failures in complex core router systems. First both a feature-categorization-based hybrid method and a changepoint-based method have been developed to detect anomalies in time-varying features with different statistical characteristics. Next, a SVM-based failure predictor is developed to predict both categories and lead time of system failures from collected anomalies. A comprehensive set of experimental results is presented for data collected during 30 days of field operation from over 20 core routers deployed by customers of a major telecom company.

The health status of core router systems needs to be analyzed efficiently in order to ensure high reliability and timely error recovery. Although a large amount operational data is collected from core routers, only a small part of this data is labeled by experts. The lack of labels is an impediment towards the adoption of supervised learning. We present an iterative self-learning procedure for assessing the health status of a core router. This procedure first computes a representative feature matrix to capture different characteristics of time-series data. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field data collected from a set of commercial core routers are used to experimentally validate the proposed health-status analyzer.

Fault tolerance is used in communication systems to ensure high reliability and rapid error recovery. The effectiveness of most proactive fault-tolerant mechanism depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the monitored data involves temporal measurements and exhibits significantly different statistical characteristics for its constituent features. We describe the design of an anomaly detector that monitors the time-series data of a complex core router system. Anomaly detection techniques are compared in terms of their effectiveness for detecting different types of anomalies. A feature-categorizing-based hybrid method is proposed to overcome the difficulty of detecting anomalies in features with different statistical characteristics. Furthermore, a correlation analyzer is implemented to remove irrelevant and redundant features. Three types of synthetic anomalies, generated using a small amount of real data for a commercial telecom system, are used to validate the proposed anomaly detector.

Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the statistical properties of the monitored data change significantly as time proceeds. We describe the design of a changepoint-based anomaly detector that first detects changepoints from collected time-series data, and then utilizes these changepoints to detect anomalies. Two approaches based on maximum-likelihood estimation are implemented to detect different types of changepoints. A clustering method is then developed to identify a wide range of normal/abnormal patterns from changepoint windows. Data collected from a set of commercial core router systems are used to validate the proposed anomaly detector. Experimental results show that our changepoint-based anomaly detector achieves better performance than traditional methods.

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) and moving-average-based trend approximation 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. Two classification methods 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.

Fault tolerance is used in communication systems to ensure high reliability and rapid error recovery. The effectiveness of most proactive fault-tolerant mechanism depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the monitored data involves temporal measurements and exhibits significantly different statistical characteristics for its constituent features. We describe the design of an anomaly detector that monitors the time-series data of a complex core router system. Anomaly detection techniques are compared in terms of their effectiveness for detecting different types of anomalies. A feature-categorizing-based hybrid method is proposed to overcome the difficulty of detecting anomalies in features with different statistical characteristics. Furthermore, a correlation analyzer is implemented to remove irrelevant and redundant features. Three types of synthetic anomalies, generated using a small amount of real data for a commercial telecom system, are used to validate the proposed anomaly detector.

Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis; these techniques are based on the historical record of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system that can handle missing syndromes and can be applied to four widely used machine-learning techniques. Several imputation methods are discussed and compared in terms of their effectiveness for addressing missing syndromes. Moreover, a syndrome-selection technique based on the minimum-redundancy-maximum-relevance criteria is also incorporated to further improve the efficiency of the proposed methods. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.

Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis, these technologies are based on historical data of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system, based on support vector machines, that can handle missing syndromes by using the method of imputation. Several imputation methods are discussed and compared in terms of their efficiency in handling missing syndromes. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.