Applying idealized lower-bound runtime models to understand inefficiencies in data-intensive computing
Authors: 
Elie Krevat, Tomer Shiran, Eric Anderson, Joseph Tucek, Jay J. Wylie, Gregory R. GangerAuthors Info & Claims
Pages 125 - 126
Abstract
No abstract available.
References
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Apache Hadoop. http://hadoop.apache.org/.
[2]
G. Ananthanarayanan, et al. Reining in the Outliers in Map-Reduce Clusters using Mantri. OSDI, 2010.
[3]
R. E. Bryant. Data-Intensive Supercomputing: The Case for DISC. Technical report. 2007. Carnegie Mellon University-CS-07-128.
[4]
J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 2008.
[5]
E. Krevat, et al. Applying Simple Performance Models to Understand Inefficiencies in Data-Intensive Computing. Technical report. 2011. Carnegie Mellon University-PDL-11-103.
[6]
E. Krevat, et al. Disks Are Like Snowflakes: No Two Are Alike. HotOS, 2011.
[7]
A. Phanishayee, et al. Measurement and Analysis of TCP Throughput Collapse in Cluster-based Storage Systems. FAST, 2008.
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- Applying idealized lower-bound runtime models to understand inefficiencies in data-intensive computing
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Information
Published In
June 2011
376 pages
ISBN:9781450308144
DOI:10.1145/1993744
- General Chair:
- Arif Merchant,
- Program Chairs:
- Kimberly Keeton,
- Dan Rubenstein
Copyright © 2011 Authors.
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 June 2011
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- Poster
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SIGMETRICS '11
Sponsor:
SIGMETRICS '11: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems
June 7 - 11, 2011
California, San Jose, USA
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Overall Acceptance Rate 459 of 2,691 submissions, 17%
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