메뉴 건너뛰기




Volumn , Issue , 2016, Pages 363-378

Ernest: Efficient performance prediction for large-scale advanced analytics

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED ANALYTICS; SYSTEMS ANALYSIS;

EID: 85077040628     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (457)

References (83)
  • 2
    • 85080728530 scopus 로고    scopus 로고
    • Apache Mahout. http://mahout.apache.org/.
  • 5
    • 85080729255 scopus 로고    scopus 로고
    • Apache Spark: Submitting Applications. http://spark.apache.org/docs/latest/submitting-applications.html.
    • Submitting Applications
  • 6
    • 85057281986 scopus 로고    scopus 로고
    • hp haven
    • Big data platform, hp haven. http://www8.hp.com/us/en/software-solutions/ big-data-platform-haven/.
    • Big Data Platform
  • 7
    • 84893290072 scopus 로고    scopus 로고
    • Common crawl. http://commoncrawl.org.
    • Common Crawl
  • 8
    • 85080694384 scopus 로고    scopus 로고
    • Hadoop History Server REST APIs. http://archive.cloudera.com/cdh4/cdh/4/hadoop/hadoop-yarn/hadoop-yarn-site/ HistoryServerRest.html.
    • Hadoop History Server REST APIs
  • 10
    • 85080696543 scopus 로고    scopus 로고
    • MapReduce Tutorial. hadoop.apache.org/ docs/current/hadoop-mapreduce-client/ hadoop-mapreduce-client-core/ MapReduceTutorial.html.
  • 15
    • 84870532645 scopus 로고    scopus 로고
    • Retrieved 9/24/2013, URL
    • Apache Hadoop NextGen MapReduce (YARN). Retrieved 9/24/2013, URL: http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html.
    • Apache Hadoop NextGen MapReduce (YARN)
  • 18
    • 0030661485 scopus 로고    scopus 로고
    • Optimizing matrix multiply using PHiPAC: A portable, high-performance, ANSI C coding methodology
    • J. Bilmes, K. Asanovic, C.-W. Chin, and J. Demmel. Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology. In Supercomputing 1997, pages 340–347.
    • (1997) Supercomputing , pp. 340-347
    • Bilmes, J.1    Asanovic, K.2    Chin, C.-W.3    Demmel, J.4
  • 21
    • 0030105185 scopus 로고    scopus 로고
    • Programming parallel algorithms
    • G. E. Blelloch. Programming parallel algorithms. Communications of the ACM, 39(3):85–97.
    • Communications of the ACM , vol.39 , Issue.3 , pp. 85-97
    • Blelloch, G.E.1
  • 23
    • 80051762104 scopus 로고    scopus 로고
    • Distributed optimization and statistical learning via the alternating direction method of multipliers
    • S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1–122, 2011.
    • (2011) Foundations and Trends in Machine Learning , vol.3 , Issue.1 , pp. 1-122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 25
    • 84877042382 scopus 로고    scopus 로고
    • A scalable cross-platform infrastructure for application performance tuning using hardware counters
    • Dallas, Texas, USA
    • S. Browne, J. Dongarra, N. Garner, K. London, and P. Mucci. A scalable cross-platform infrastructure for application performance tuning using hardware counters. In Supercomputing 2000, Dallas, Texas, USA.
    • Supercomputing 2000
    • Browne, S.1    Dongarra, J.2    Garner, N.3    London, K.4    Mucci, P.5
  • 29
  • 31
    • 0343462141 scopus 로고    scopus 로고
    • Automated empirical optimizations of software and the ATLAS project
    • R. Clint Whaley, A. Petitet, and J. J. Dongarra. Automated empirical optimizations of software and the ATLAS project. Parallel Computing, 27(1):3–35, 2001.
    • (2001) Parallel Computing , vol.27 , Issue.1 , pp. 3-35
    • Clint Whaley, R.1    Petitet, A.2    Dongarra, J.J.3
  • 32
    • 84872575253 scopus 로고    scopus 로고
    • Learning feature representations with k-means
    • Springer
    • A. Coates and A. Y. Ng. Learning feature representations with k-means. In Neural Networks: Tricks of the Trade, pages 561–580. Springer, 2012.
    • (2012) Neural Networks: Tricks of the Trade , pp. 561-580
    • Coates, A.1    Ng, A.Y.2
  • 35
    • 37549003336 scopus 로고    scopus 로고
    • MapReduce: Simplified data processing on large clusters
    • J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 2008.
    • (2008) Communications of the ACM , vol.51 , Issue.1
    • Dean, J.1    Ghemawat, S.2
  • 36
    • 84897791438 scopus 로고    scopus 로고
    • Quasar: Resource-efficient and qos-aware cluster management
    • C. Delimitrou and C. Kozyrakis. Quasar: Resource-efficient and qos-aware cluster management. In ASPLOS 2014, pages 127–144.
    • (2014) ASPLOS , pp. 127-144
    • Delimitrou, C.1    Kozyrakis, C.2
  • 37
  • 39
    • 84860561660 scopus 로고    scopus 로고
    • Jockey: Guaranteed job latency in data parallel clusters
    • A. D. Ferguson, P. Bodik, S. Kandula, E. Boutin, and R. Fonseca. Jockey: guaranteed job latency in data parallel clusters. In Eurosys 2012, pages 99–112.
    • (2012) Eurosys , pp. 99-112
    • Ferguson, A.D.1    Bodik, P.2    Kandula, S.3    Boutin, E.4    Fonseca, R.5
  • 41
    • 36849072045 scopus 로고    scopus 로고
    • Graph implementations for nonsmooth convex programs
    • Blondel, S. Boyd, and H. Kimura, editors, Springer-Verlag Limited
    • M. Grant and S. Boyd. Graph implementations for nonsmooth convex programs. In V. Blondel, S. Boyd, and H. Kimura, editors, Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences, pages 95–110. Springer-Verlag Limited, 2008. http://stanford.edu/~boyd/graph_dcp.html.
    • (2008) Recent Advances in Learning and Control, Lecture Notes in Control and Information Sciences , pp. 95-110
    • Grant, M.1    Boyd, S.2
  • 43
    • 79960425522 scopus 로고    scopus 로고
    • Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
    • N. Halko, P.-G. Martinsson, and J. A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM review, 53(2):217–288, 2011.
    • (2011) SIAM Review , vol.53 , Issue.2 , pp. 217-288
    • Halko, N.1    Martinsson, P.-G.2    Tropp, J.A.3
  • 45
    • 85018503656 scopus 로고    scopus 로고
    • Reducing file system tail latencies with chopper
    • Santa Clara, CA
    • J. He, D. Nguyen, A. Arpaci-Dusseau, and R. Arpaci-Dusseau. Reducing file system tail latencies with chopper. In FAST 2015, pages 119–133, Santa Clara, CA.
    • (2015) FAST , pp. 119-133
    • He, J.1    Nguyen, D.2    Arpaci-Dusseau, A.3    Arpaci-Dusseau, R.4
  • 46
    • 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. VLDB 2011, 4(11):1111–1122.
    • (2011) VLDB , vol.4 , Issue.11 , pp. 1111-1122
    • Herodotou, H.1    Babu, S.2
  • 47
    • 82155186182 scopus 로고    scopus 로고
    • No one (cluster) size fits all: Automatic cluster sizing for data-intensive analytics
    • H. Herodotou, F. Dong, and S. Babu. No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics. In SOCC 2011.
    • (2011) SOCC
    • Herodotou, H.1    Dong, F.2    Babu, S.3
  • 48
    • 80053500227 scopus 로고    scopus 로고
    • Starfish: A self-tuning system for big data analytics
    • H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, and S. Babu. Starfish: A self-tuning system for big data analytics. In CIDR, volume 11, pages 261–272, 2011.
    • (2011) CIDR , vol.11 , pp. 261-272
    • Herodotou, H.1    Lim, H.2    Luo, G.3    Borisov, N.4    Dong, L.5    Cetin, F.B.6    Babu, S.7
  • 51
    • 35448961922 scopus 로고    scopus 로고
    • Dryad: Distributed data-parallel programs from sequential building blocks
    • M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In Eurosys 2007.
    • (2007) Eurosys
    • Isard, M.1    Budiu, M.2    Yu, Y.3    Birrell, A.4    Fetterly, D.5
  • 57
    • 77954732118 scopus 로고    scopus 로고
    • Para-timer: A progress indicator for mapreduce dags
    • Indianapolis, Indiana, USA
    • K. Morton, M. Balazinska, and D. Grossman. Para-timer: A progress indicator for mapreduce dags. In SIGMOD 2010, pages 507–518, Indianapolis, Indiana, USA.
    • SIGMOD 2010 , pp. 507-518
    • Morton, K.1    Balazinska, M.2    Grossman, D.3
  • 65
    • 84974666914 scopus 로고    scopus 로고
    • Matchmaking: Distributed resource management for high throughput computing
    • R. Raman, M. Livny, and M. Solomon. Matchmaking: Distributed resource management for high throughput computing. In HPDC 1998.
    • (1998) HPDC
    • Raman, R.1    Livny, M.2    Solomon, M.3
  • 68
    • 84905856702 scopus 로고    scopus 로고
    • Mrtuner: A toolkit to enable holistic optimization for MapReduce jobs
    • J. Shi, J. Zou, J. Lu, Z. Cao, S. Li, and C. Wang. MRTuner: A Toolkit to Enable Holistic Optimization for MapReduce Jobs. VLDB 2014, 7(13).
    • (2014) VLDB , vol.7 , Issue.13
    • Shi, J.1    Zou, J.2    Lu, J.3    Cao, Z.4    Li, S.5    Wang, C.6
  • 69
    • 85067359901 scopus 로고    scopus 로고
    • Cutting corners: Workbench automation for server benchmarking
    • P. Shivam, V. Marupadi, J. Chase, T. Subramaniam, and S. Babu. Cutting corners: workbench automation for server benchmarking. In USENIX ATC 2008, pages 241–254.
    • (2008) USENIX ATC , pp. 241-254
    • Shivam, P.1    Marupadi, V.2    Chase, J.3    Subramaniam, T.4    Babu, S.5
  • 72
    • 77954526823 scopus 로고    scopus 로고
    • The case for cloud computing in genome informatics
    • L. D. Stein et al. The case for cloud computing in genome informatics. Genome Biology, 11(5):207, 2010.
    • (2010) Genome Biology , vol.11 , Issue.5 , pp. 207
    • Stein, L.D.1
  • 75
    • 0031123769 scopus 로고    scopus 로고
    • SUMMA: Scalable universal matrix multiplication algorithm
    • R. A. Van De Geijn and J. Watts. SUMMA: Scalable universal matrix multiplication algorithm. Concurrency-Practice and Experience, 9(4):255–274, 1997.
    • (1997) Concurrency-Practice and Experience , vol.9 , Issue.4 , pp. 255-274
    • Van De Geijn, R.A.1    Watts, J.2
  • 77
    • 79960196705 scopus 로고    scopus 로고
    • Aria: Automatic resource inference and allocation for mapreduce environments
    • Karlsruhe, Germany
    • A. Verma, L. Cherkasova, and R. H. Campbell. Aria: Automatic resource inference and allocation for mapreduce environments. In ICAC 2011, pages 235–244, Karlsruhe, Germany.
    • (2011) ICAC , pp. 235-244
    • Verma, A.1    Cherkasova, L.2    Campbell, R.H.3
  • 80
    • 85118316888 scopus 로고    scopus 로고
    • Wrangler: Predictable and faster jobs using fewer resources
    • N. J. Yadwadkar, G. Ananthanarayanan, and R. Katz. Wrangler: Predictable and faster jobs using fewer resources. In SOCC 2014.
    • (2014) SOCC
    • Yadwadkar, N.J.1    Ananthanarayanan, G.2    Katz, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.