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Home » Theia: Visual Signatures for Problem Diagnosis in Large Hadoop Clusters
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Theia: Visual Signatures for Problem Diagnosis in Large Hadoop Clusters

Authors: 

Elmer Garduno, Soila P. Kavulya, Jiaqi Tan, Rajeev Gandhi, and Priya Narasimhan, Carnegie Mellon University
Awarded Best Student Paper!   

Abstract: 

Diagnosing performance problems in large distributed systems can be daunting as the copious volume of monitoring information available can obscure the root-cause of the problem. Automated diagnosis tools help narrow down the possible root-causes—however, these tools are not perfect thereby motivating the need for visualization tools that allow users to explore their data and gain insight on the root-cause. In this paper we describe Theia, a visualization tool that analyzes application-level logs in a Hadoop cluster, and generates visual signatures of each job's performance. These visual signatures provide compact representations of task durations, task status, and data consumption by jobs. We demonstrate the utility of Theia on real incidents experienced by users on a production Hadoop cluster.

Elmer Garduno, Carnegie Mellon University

Soila P. Kavulya, Carnegie Mellon University

Jiaqi Tan, DSO National Laboratories, Singapore

Rajeev Gandhi, Carnegie Mellon University

Priya Narasimhan, Carnegie Mellon University

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BibTeX
@inproceedings {180338,
author = {Elmer Garduno and Soila P. Kavulya and Jiaqi Tan and Rajeev Gandhi and Priya Narasimhan},
title = {Theia: Visual Signatures for Problem Diagnosis in Large Hadoop Clusters },
booktitle = {26th Large Installation System Administration Conference (LISA 12)},
year = {2012},
isbn = {978-931971-97-3},
address = {San Diego, CA},
pages = {33--42},
url = {https://www.usenix.org/conference/lisa12/technical-sessions/presentation/garduno},
publisher = {USENIX Association},
month = dec,
}
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Award: 
Best Student Paper
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