Predicting execution bottlenecks in map-reduce clusters

4th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2012

Volumn , Issue , 2012, Pages

  Bortnikov, Edward ^a   Frank, Ari ^b   Hillel, Eshcar ^a   Rao, Sriram ^a  

^a YAHOO RESEARCH   (United States)

^b Affectivon Inc   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; COMPUTER SCIENCE;

MACHINE LEARNING APPROACHES; MAP-REDUCE; PERFORMANCE BOTTLENECKS; REMOTE DATA ACCESS; RUNTIMES; SCHEDULING DECISIONS; SPECULATIVE EXECUTION; TASK-SCHEDULING;

CLOUD COMPUTING;

EID: 84906706410     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. G. Ananthanarayanan, S. Kandula, A. Greenberg, I. Stoica, Y. Lu, B. Saha, and E. Harris. Reining in the outliers in map-reduce clusters using mantri. In ACM OSDI, 2010.
2. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51:107-113, Jan. 2008.
3. J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29:1189-1232, 2001.
4. S. Kavulya, J. Tany, R. Gandhi, and P. Narasimhan. An analysis of traces from a production mapreduce cluster. In IEEE/ACM CCGrid, 2010.
5. R. G. D. Steel and J. H. Torrie. Principles and Procedures of Statistics. McGraw-Hill, 1960.
6. T. White. Hadoop: The Definitive Guide. O'Reilly Media / Yahoo Press, 2 edition, 2010.
7. M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In Eurosys, 2010.
8. M. Zaharia, A. Kowinski, A. D. Joseph, R. Katz, and I. Stoica. Improving mapreduce performance in heterogeneous environments. In ACM OSDI, 2008.
9. Q. Zhang, J. Hellerstein, and R. Boutaba. Characterizing task usage shapes in google compute clusters. In LADIS, 2011.