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Monitoring Hadoop through Tracing

This project aims to enhance Hadoop monitoring by utilizing tracing techniques to capture causality, machine learning to detect problems and unsupervised learning to identify unusual runs. Additionally, it plans to analyze X-Trace reports in real-time and scale data collection for better Hadoop performance.

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Monitoring Hadoop through Tracing

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  1. Monitoring Hadoop through Tracing Andy Konwinski and Matei Zaharia

  2. Objectives • Debug and profile data center applications • Hadoop file system and map-reduce • Apache Nutch web indexing engine • Automatically detect problems from traces

  3. State-of-the-Art • Unpublished proprietary log management systems at Google, Yahoo, etc • Per-machine logs • Sawzall for mining log data • Node monitoring daemon (System Health Infrastructure)

  4. Our Idea • Capture causality directly by tracing computations across nodes using X-Trace • Use machine learning to detect problems • Detect unusual runs using unsupervised learning • Classify problems using supervised learning • Also want to study Hadoop performance

  5. Risks • Scaling X-Trace data collection • Analyzing X-Trace reports in real time • Identifying features of X-Trace graphs to run machine learning on • Our manually induced errors may not capture all failures that happen in a production cluster

  6. The Plan

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