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A User-Friendly Log Viewer for Storage Systems

Published: 12 May 2016 Publication History

Abstract

System log files contains messages emitted from several modules within a system and carries valuable information about the system state such as device status and error conditions and also about the various tasks within the system such as program names, execution path, including function names and parameters, and the task completion status. For customers with remote support, the system collects and transmits these logs to a central enterprise repository, where these are monitored for alerts, problem forecasting, and troubleshooting.
Very large log files limit the interpretability for the support engineers. For an expert, a large volume of log messages may not pose any problem; however, an inexperienced person may get flummoxed due to the presence of a large number of log messages. Often it is desired to present the log messages in a comprehensive manner where a person can view the important messages first and then go into details if required.
In this article, we present a user-friendly log viewer where we first hide the unimportant or inconsequential messages from the log file. A user can then click a particular hidden view and get the details of the hided messages. Messages with low utility are considered inconsequential as their removal does not impact the end user for the aforesaid purpose such as problem forecasting or troubleshooting. We relate the utility of a message to the probability of its appearance in the due context. We present machine-learning-based techniques that computes the usefulness of individual messages in a log file. We demonstrate identification and discarding of inconsequential messages to shrink the log size to acceptable limits. We have tested this over real-world logs and observed that eliminating such low value data can reduce the log files significantly (30% to 55%), with minimal error rates (7% to 20%). When limited user feedback is available, we show modifications to the technique to learn the user intent and accordingly further reduce the error.

References

[1]
R. Agrawal, T. Imieliński, and A. Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD’93). ACM New York, NY, 207--216.
[2]
S. Alspaugh, Beidi Chen, Jessica Lin, Archana Ganapathi, Marti Hearst, and Randy Katz. 2014. Analyzing log analysis: An empirical study of user log mining. In 28th Large Installation System Administration Conference (LISA14). USENIX Association, Seattle, WA, 62--77.
[3]
Christopher J. C. Burges. 1998. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 2 (1998), 121--167.
[4]
Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recog. Lett. 27, 8 (2006), 861--874.
[5]
Anil K. Jain, Jianchang Mao, and K. Moidin Mohiuddin. 1996. Artificial neural networks: A tutorial. Computer 29, 3 (1996), 31--44.
[6]
Weihang Jiang, Chongfeng Hu, Shankar Pasupathy, Arkady Kanevsky, Zhenmin Li, and Yuanyuan Zhou. 2009. Understanding customer problem troubleshooting from storage system logs. In FAST, Vol. 9. 43--56.
[7]
J. Koshy. 2007. PMC based Performance Measurement in FreeBSD. Retrieved from http://people.freebsd.org/∼jkoshy/projects/perf-measurement.
[8]
Time Kramer. 2003. Effective Log Reduction and Analysis Using Linux and Open Source Tools. Retrieved from http://www.giac.org/paper/gsec/3144/effective-log-reduction-analysis-linux-open-source-tools/105234.
[9]
Yinglung Liang, Yanyong Zhang, Hui Xiong, and Ramendra Sahoo. 2007. An adaptive semantic filter for blue gene/L failure log analysis. In Proceedings of the 3rd International Workshop on System Management Techniques, Processes, and Services (SMTPS).
[10]
Andrew L. Maas and Andrew Y. Ng. 2010. A probabilistic model for semantic word vectors. In Proceedings of the Workshop on Deep Learning and Unsupervised Feature Learning, NIPS, Vol. 10.
[11]
Network Appliance. 2007. Proactive health management with autosupport. http://www.netapp.com/us/media/wp-7027.pdf.
[12]
W. Peng, T. Li, and S. Ma. 2005. Mining logs files for data-driven system management. ACM SIGKDD Explor. Newslett. 7, 1 (2005), 44--51.
[13]
Christian S. Perone. 2009. Pyevolve: A Python open-source framework for genetic algorithms. SIGEVOlution 4, 1 (Nov. 2009), 12--20.
[14]
R. R. Sarukkai. 2000. Link prediction and path analysis using Markov chains. In Proceedings of the 9th International World Wide Web Conference on Computer Networks : The International Journal of Computer and Telecommunications Netowrking. North-Holland Publishing Co., Amsterdam, The Netherlands, 377--386.
[15]
S. A. Shahrestani, M. Feily, R. Ahmad, and S. Ramadass. 2010. Discovery of invariant BOT behaviour through visual network monitoring system. In Proceedings of the 2010 Fourth International Conference on Emerging Security Information, Systems and Technologies. 182--188.
[16]
Darrell Whitley. 1994. A genetic algorithm tutorial. Stat. Comput. 4, 2 (1994), 65--85.
[17]
Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael Jordan. 2010. Experience mining google.s production console logs. In Proceedings of the SLAML (2010).
[18]
Z. Ziming, L. Zhiling, B. H. Park, and A. Geist. 2009. System log pre-processing to improve failure prediction. In Proceedings of the IEEE/IFIP International Conference on Dependable Systems & Networks (DSN’’09). 572--577.

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  • (2020)Bibliometric survey of IT Infrastructure Management to Avoid Failure ConditionsInformation Discovery and Delivery10.1108/IDD-06-2020-0060ahead-of-print:ahead-of-printOnline publication date: 27-Nov-2020
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Published In

cover image ACM Transactions on Storage
ACM Transactions on Storage  Volume 12, Issue 3
June 2016
237 pages
ISSN:1553-3077
EISSN:1553-3093
DOI:10.1145/2932205
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 May 2016
Accepted: 01 November 2015
Revised: 01 July 2015
Received: 01 January 2015
Published in TOS Volume 12, Issue 3

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Author Tags

  1. Log reduction
  2. filtering
  3. learning

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Cited By

View all
  • (2024)Reliability through an optimal SDS controller’s placement in a SDDC and smart cityCluster Computing10.1007/s10586-024-04325-627:6(7219-7240)Online publication date: 1-Sep-2024
  • (2023)Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended AbstractProceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3573900.3593632(50-51)Online publication date: 21-Jun-2023
  • (2020)Bibliometric survey of IT Infrastructure Management to Avoid Failure ConditionsInformation Discovery and Delivery10.1108/IDD-06-2020-0060ahead-of-print:ahead-of-printOnline publication date: 27-Nov-2020
  • (2019)Graphs are not enoughProceedings of the 11th USENIX Conference on Hot Topics in Storage and File Systems10.5555/3357062.3357069(5-5)Online publication date: 8-Jul-2019
  • (2019)Towards an Efficient Performance Testing Through Dynamic Workload AdaptationTesting Software and Systems10.1007/978-3-030-31280-0_13(215-233)Online publication date: 15-Oct-2019
  • (2017)Solution Recommender for System Failure Recovery via Log Event Pattern Matching on a Knowledge GraphProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3095094(331-334)Online publication date: 8-Jun-2017

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