Springer Anomaly-Detection and Health-Analysis Techniques for Core Router Systems

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes 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 for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status. From the Back Cover This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today’s Internet Protocol (IP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes 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 for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; Includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status. About the Author Shi Jin is a Senior DFT Engineer at Nvidia Corporation, in Santa Clara, California. Zhaobo Zhang is a Staff Engineer at Huawei Technologies, in Santa Clara, California. Krish Chakrabarty is the William H. Younger Distinguished Professor of Engineering in the Department of Electrical and Computer Engineering, at Duke University in Durham, NC. He has been at Duke University since 1998. His current research is focused on: testing and design-for-testability of integrated circuits (especially 3D and multicore chips); digital microfluidics, biochips, and cyberphysical systems; optimization of digital print and production system infrastructure. His research projects in the recent past have also included chip cooling using digital microfluidics, wireless sensor networks, and real-time embedded systems. Research support is provided by the National Science Foundation, the Semiconductor Research Corporation, Cisco Systems, HP Labs, Huawei Technologies, and Intel Corporation through Intel Lab's Academic Research Office. Other sponsors in the past have included National Institutes of Health , DARPA and the Office of Naval Research. Prof. Chakrabarty received the B. Tech. degree from the Indian Institute of Technology, Kharagpur, India in 1990, and the M.S.E. and Ph.D. degrees from the University of Michigan, Ann Arbor in 1992 and 1995, respectively, all in Com