Event Sequence Identification and Deep Learning Classification for Anomaly Detection and Predication on High-Performance Computing Systems

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High-performance computing (HPC) systems continue growing in both scale and complexity. These large-scale, heterogeneous systems generate tens of millions of log messages every day. Effective log analysis for understanding system behaviors and identifying system anomalies and failures is highly challenging. Existing log analysis approaches use line-by-line message processing. They are not effective for discovering subtle behavior patterns and their transitions, and thus may overlook some critical anomalies. In this dissertation research, I propose a system log event block detection (SLEBD) method which can extract the log messages that belong to a component or system event into an event block (EB) … continued below

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xi, 95 pages

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Li, Zongze December 2019.

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  • Li, Zongze

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High-performance computing (HPC) systems continue growing in both scale and complexity. These large-scale, heterogeneous systems generate tens of millions of log messages every day. Effective log analysis for understanding system behaviors and identifying system anomalies and failures is highly challenging. Existing log analysis approaches use line-by-line message processing. They are not effective for discovering subtle behavior patterns and their transitions, and thus may overlook some critical anomalies. In this dissertation research, I propose a system log event block detection (SLEBD) method which can extract the log messages that belong to a component or system event into an event block (EB) accurately and automatically. At the event level, we can discover new event patterns, the evolution of system behavior, and the interaction among different system components. To find critical event sequences, existing sequence mining methods are mostly based on the a priori algorithm which is compute-intensive and runs for a long time. I develop a novel, topology-aware sequence mining (TSM) algorithm which is efficient to generate sequence patterns from the extracted event block lists. I also train a long short-term memory (LSTM) model to cluster sequences before specific events. With the generated sequence pattern and trained LSTM model, we can predict whether an event is going to occur normally or not. To accelerate such predictions, I propose a design flow by which we can convert recurrent neural network (RNN) designs into register-transfer level (RTL) implementations which are deployed on FPGAs. Due to its high parallelism and low power, FPGA achieves a greater speedup and better energy efficiency compared to CPU and GPU according to our experimental results.

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xi, 95 pages

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  • December 2019

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  • Jan. 24, 2020, 6:07 a.m.

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  • Sept. 25, 2021, 1:31 p.m.

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Li, Zongze. Event Sequence Identification and Deep Learning Classification for Anomaly Detection and Predication on High-Performance Computing Systems, dissertation, December 2019; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc1609172/: accessed June 29, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

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