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Volumn , Issue , 2011, Pages

No one (cluster) size fits all: Automatic cluster sizing for data-intensive analytics

Author keywords

Cloud computing; Cluster provisioning; MapReduce

Indexed keywords

AUTOMATED TECHNIQUES; BLACK BOXES; CLUSTER PROVISIONING; CLUSTER SIZES; COMPLEX CLUSTERS; COMPREHENSIVE EVALUATION; MAPREDUCE; WHITE-BOX MODELS;

EID: 82155186182     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2038916.2038934     Document Type: Conference Paper
Times cited : (207)

References (29)
  • 1
    • 82155174848 scopus 로고    scopus 로고
    • Amazon Elastic MapReduce. http://aws.amazon.com/elasticmapreduce.
  • 2
    • 77954942463 scopus 로고    scopus 로고
    • Towards automatic optimization of MapReduce programs
    • S. Babu. Towards Automatic Optimization of MapReduce Programs. In SOCC, pages 137-142, 2010.
    • (2010) SOCC , pp. 137-142
    • Babu, S.1
  • 3
    • 85033461284 scopus 로고    scopus 로고
    • Statistical machine learning makes automatic control practical for internet datacenters
    • P. Bodik, R. Griffith, C. Sutton, A. Fox, M. Jordan, and D. Patterson. Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. In HotCloud, 2009.
    • (2009) HotCloud
    • Bodik, P.1    Griffith, R.2    Sutton, C.3    Fox, A.4    Jordan, M.5    Patterson, D.6
  • 7
    • 84941157611 scopus 로고    scopus 로고
    • Primitives for workload summarization and implications for SQL
    • S. Chaudhuri, P. Ganesan, and V. R. Narasayya. Primitives for Workload Summarization and Implications for SQL. In VLDB, pages 730-741, 2003.
    • (2003) VLDB , pp. 730-741
    • Chaudhuri, S.1    Ganesan, P.2    Narasayya, V.R.3
  • 9
    • 82155169979 scopus 로고    scopus 로고
    • Cloudera: 7 tips for Improving MapReduce Performance. http://www.cloudera.com/blog/2009/12/ 7-tips-for-improving-mapreduce- performance/.
  • 10
    • 37549003336 scopus 로고    scopus 로고
    • MapReduce: Simplified data processing on large clusters
    • J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Commun. ACM, 51(1):107-113, 2008.
    • (2008) Commun. ACM , vol.51 , Issue.1 , pp. 107-113
    • Dean, J.1    Ghemawat, S.2
  • 11
    • 79953889531 scopus 로고    scopus 로고
    • Tuning database configuration parameters with iTuned
    • S. Duan, V. Thummala, and S. Babu. Tuning Database Configuration Parameters with iTuned. PVLDB, 2(1):1246-1257, 2009.
    • (2009) PVLDB , vol.2 , Issue.1 , pp. 1246-1257
    • Duan, S.1    Thummala, V.2    Babu, S.3
  • 13
    • 82155169982 scopus 로고    scopus 로고
    • H. Herodotou. Hadoop Performance Models. Technical report, Duke Univ., 2010. http://www.cs.duke.edu/starfish/files/hadoop-models.pdf.
    • (2010)
    • Herodotou, H.1
  • 14
    • 82155174846 scopus 로고    scopus 로고
    • Profiling what-if analysis and cost-based optimization of MapReduce programs
    • H. Herodotou and S. Babu. Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs. PVLDB, 4, 2011.
    • (2011) PVLDB , vol.4
    • Herodotou, H.1    Babu, S.2
  • 16
    • 77954585266 scopus 로고    scopus 로고
    • HadoopToSQL: A MapReduce query optimizer
    • M.-Y. Iu and W. Zwaenepoel. HadoopToSQL: A MapReduce Query Optimizer. In EuroSys, pages 251-264, 2010.
    • (2010) EuroSys , pp. 251-264
    • Iu, M.-Y.1    Zwaenepoel, W.2
  • 17
    • 84863535860 scopus 로고    scopus 로고
    • Automatic optimization of MapReduce programs
    • E. Jahani, M. J. Cafarella, and C. Ré. Automatic Optimization of MapReduce Programs. PVLDB, 4:386-396, 2011.
    • (2011) PVLDB , vol.4 , pp. 386-396
    • Jahani, E.1    Cafarella, M.J.2    Ré, C.3
  • 18
    • 84981321777 scopus 로고    scopus 로고
    • Towards optimizing hadoop provisioning in the cloud
    • K. Kambatla, A. Pathak, and H. Pucha. Towards Optimizing Hadoop Provisioning in the Cloud. In HotCloud, 2009.
    • (2009) HotCloud
    • Kambatla, K.1    Pathak, A.2    Pucha, H.3
  • 20
    • 82155174845 scopus 로고    scopus 로고
    • Data-intensive text processing with MapReduce
    • J. Lin and C. Dyer. Data-Intensive Text Processing with MapReduce. Morgan and Claypool, 2010.
    • (2010) Morgan and Claypool
    • Lin, J.1    Dyer, C.2
  • 21
    • 36348946355 scopus 로고    scopus 로고
    • Modeling the relative fitness of storage
    • DOI 10.1145/1269899.1254887, SIGMETRICS'07 - Proceedings of the 2007 International Conference on Measurement and Modeling of Computer Systems
    • M. Mesnier, M. Wachs, R. Sambasivan, A. Zheng, and G. Ganger. Modeling the Relative Fitness of Storage. SIGMETRICS, 35(1):37-48, 2007. (Pubitemid 350158071)
    • (2007) Performance Evaluation Review , vol.35 , Issue.1 , pp. 37-48
    • Mesnier, M.P.1    Wachs, M.2    Sambasivan, R.R.3    Zheng, A.X.4    Ganger, G.R.5
  • 22
    • 82155178357 scopus 로고    scopus 로고
    • Mumak: Map-Reduce Simulator. https://issues.apache.org/jira/browse/ MAPREDUCE-728.
  • 23
    • 82155178356 scopus 로고    scopus 로고
    • OpenCore Probes vs Sun BTrace. http://opencore.jinspired.com/?pagefiid= 588.
  • 26
    • 76349087821 scopus 로고    scopus 로고
    • A simulation approach to evaluating design decisions in MapReduce setups
    • G. Wang, A. R. Butt, P. Pandey, and K. Gupta. A Simulation Approach to Evaluating Design Decisions in MapReduce Setups. In MASCOTS, pages 1-11, 2009.
    • (2009) MASCOTS , pp. 1-11
    • Wang, G.1    Butt, A.R.2    Pandey, P.3    Gupta, K.4
  • 28
    • 26444446303 scopus 로고    scopus 로고
    • A recursive random search algorithm for large-scale network parameter configuration
    • T. Ye and S. Kalyanaraman. A Recursive Random Search Algorithm for Large-scale Network Parameter Configuration. In SIGMETRICS, pages 196-205, 2003.
    • (2003) SIGMETRICS , pp. 196-205
    • Ye, T.1    Kalyanaraman, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.