Resource elasticity for large-scale machine learning

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn 2015-May, Issue , 2015, Pages 137-152

  Huang, Botong ^b   Boehm, Matthias ^a   Tian, Yuanyuan ^a   Reinwald, Berthold ^a   Tatikonda, Shirish ^a   Reiss, Frederick R ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELASTICITY;

AUTOMATIC GENERATION; CLUSTER CONFIGURATIONS; DISTRIBUTED COMPUTATIONS; LARGE-SCALE MACHINE LEARNING; MEMORY COMPUTATIONS; MEMORY CONFIGURATION; MEMORY CONSTRAINTS; RESOURCE NEGOTIATION;

LEARNING SYSTEMS;

EID: 84957596678     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2723372.2749432     Document Type: Conference Paper

References (54)

1. D. Abadi et al. The Beckman Report on Database Research. SIGMOD Record, 43(3), 2014.
2. S. Agrawal, E. Chu, and V. R. Narasayya. Automatic Physical Design Tuning: Workload as a Sequence. In SIGMOD, 2006.
3. Apache. Mahout: Scalable machine learning and data mining. https://mahout.apache.org/.
4. M. Boehm. Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs. ArXiv e-prints, 2015. arXiv:1503.06384 [cs. DC].
5. M. Boehm, D. R. Burdick, A. V. Evfimievski, B. Reinwald, F. R. Reiss, P. Sen, S. Tatikonda, and Y. Tian. SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. IEEE Data Eng. Bull., 37(3), 2014.
6. M. Boehm, S. Tatikonda, B. Reinwald, P. Sen, Y. Tian, D. Burdick, and S. Vaithyanathan. Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. PVLDB, 7(7), 2014.
7. V. R. Borkar, Y. Bu, M. J. Carey, J. Rosen, N. Polyzotis, T. Condie, M. Weimer, and R. Ramakrishnan. Declarative Systems for Large-Scale Machine Learning. IEEE Data Eng. Bull., 35 (2).
8. N. Bruno and R. V. Nehme. Configuration-Parametric Query Optimization for Physical Design Tuning. In SIGMOD, 2008.
9. B. Chun, T. Condie, C. Curino, R. Ramakrishnan, R. Sears, and M. Weimer. REEF: Retainable Evaluator Execution Framework. PVLDB, 6(12), 2013.
10. J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton. MAD Skills: New Analysis Practices for Big Data. PVLDB, 2(2), 2009.
11. T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer. Machine Learning for Big Data. In SIGMOD, 2013.
12. A. Crotty, A. Galakatos, K. Dursun, T. Kraska, U. Cetintemel, and S. Zdoni. Tupleware: "Big" Data, Big Analytics, Small Clusters. In CIDR, 2015.
13. A. Crotty, A. Galakatos, and T. Kraska. Tupleware: Distributed Machine Learning on Small Clusters. IEEE Data Eng. Bull., 37(3), 2014.
14. S. Das, S. Nishimura, D. Agrawal, and A. El Abbadi. Albatross: Lightweight Elasticity in Shared Storage Databases for the Cloud using Live Data Migration. PVLDB, 4(8), 2011.
15. J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In OSDI, 2004.
16. A. Deshpande, Z. G. Ives, and V. Raman. Adaptive Query Processing. Foundations and Trends in Databases, 1(1), 2007.
17. J. Duggan and M. Stonebraker. Incremental Elasticity for Array Databases. In SIGMOD, 2014.
18. M. Ead, H. Herodotou, A. Aboulnaga, and S. Babu. PStorM: Profile Storage and Matching for Feedback-Based Tuning of MapReduce Jobs. In EDBT, 2014.
19. A. J. Elmore, S. Das, D. Agrawal, and A. El Abbadi. Zephyr: Live Migration in Shared Nothing Databases for Elastic Cloud Platforms. In SIGMOD, 2011.
20. A. Ghoting, R. Krishnamurthy, E. P. D. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, and S. Vaithyanathan. SystemML: Declarative Machine Learning on MapReduce. In ICDE, 2011.
21. H. Herodotou and S. Babu. Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs. PVLDB, 4(11), 2011.
22. H. Herodotou, F. Dong, and S. Babu. No One (Cluster) Size Fits All: Automatic Cluster Sizing for Data-intensive Analytics. In SOCC, 2011.
23. B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. H. Katz, S. Shenker, and I. Stoica. Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center. In NSDI, 2011.
24. B. Huang, S. Babu, and J. Yang. Cumulon: Optimizing Statistical Data Analysis in the Cloud. In SIGMOD, 2013.
25. B. Huang, N. W. D. Jarrett, S. Babu, S. Mukherjee, and J. Yang. Cumulon: Cloud-Based Statistical Analysis from Users Perspective. IEEE Data Eng. Bull., 37(3), 2014.
26. K. Kambatla, A. Pathak, and H. Pucha. Towards Optimizing Hadoop Provisioning in the Cloud. In HotCloud, 2009.
27. K. Karanasos, A. Balmin, M. Kutsch, F. Ozcan, V. Ercegovac, C. Xia, and J. Jackson. Dynamically Optimizing Queries over Large Scale Data Platforms. In SIGMOD, 2014.
28. D. Kernert, F. Koehler, and W. Lehner. SpMachO - Optimizing Sparse Linear Algebra Expressions with Probabilistic Density Estimation. In EDBT, 2015.
29. N. Khoussainova, M. Balazinska, and D. Suciu. PerfXplain: Debugging MapReduce Job Performance. PVLDB, 5(7), 2012.
30. H. Kimura, V. R. Narasayya, and M. Syamala. Compression Aware Physical Database Design. PVLDB, 4(10), 2011.
31. D. Lyubimov. Mahout Scala Bindings and Mahout Spark Bindings for Linear Algebra Subroutines. Apache, 2014. http://mahout.apache.org/users/sparkbindings/ScalaSparkBindings.pdf.
32. V. Markl. Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era. PVLDB, 7(13), 2014.
33. K. Morton, M. Balazinska, and D. Grossman. ParaTimer: A Progress Indicator for MapReduce DAGs. In SIGMOD, 2010.
34. A. C. Murthy, V. K. Vavilapalli, D. Eadline, J. Niemiec, and J. Markham. Apache Hadoop YARN: Moving Beyond Mapreduce and Batch Processing with Apache Hadoop 2. Pearson Education, 2013.
35. S. Narayanamurthy, M. Weimer, D. Mahajan, T. Condie, S. Sellamanickam, and K. Selvaraj. Towards Resource-Elastic Machine Learning. In NIPS workshop Biglearn, 2013.
36. S. Papadomanolakis, D. Dash, and A. Ailamaki. Efficient Use of the Query Optimizer for Automated Database Design. In VLDB, 2007.
37. C. Reiss and R. H. Katz. Recommending Just Enough Memory for Analytics. In SOCC, 2013.
38. F. Reiss and T. Kanungo. A Characterization of the Sensitivity of Query Optimization to Storage Access Cost Parameters. In SIGMOD, 2003.
39. A. Rowstron, D. Narayanan, A. Donnelly, G. O'Shea, and A. Douglas. Nobody Ever Got Fired for Using Hadoop on a Cluster. In HotCDP, 2012.
40. S. Schelter, C. Boden, M. Schenck, A. Alexandrov, and V. Markl. Distributed Matrix Factorization with MapReduce using a series of Broadcast-Joins. In RecSys, 2013.
41. M. Schwarzkopf, A. Konwinski, M. Abd-El-Malek, and J. Wilkes. Omega: Flexible, Scalable Schedulers for Large Compute Clusters. In EuroSys, 2013.
42. M. Serafini, E. Mansour, A. Aboulnaga, K. Salem, T. Rafiq, and U. F. Minhas. Accordion: Elastic Scalability for Database Systems Supporting Distributed Transactions. PVLDB, 7(12), 2014.
43. J. Shi, J. Zou, J. Lu, Z. Cao, S. Li, and C. Wang. MRTuner: A Toolkit to Enable Holistic Optimization for MapReduce Jobs. PVLDB, 7(13), 2014.
44. M. A. Soliman, L. Antova, V. Raghavan, A. El-Helw, Z. Gu, E. Shen, G. C. Caragea, C. Garcia-Alvarado, F. Rahman, M. Petropoulos, F. Waas, S. Narayanan, K. Krikellas, and R. Baldwin. Orca: A Modular Query Optimizer Architecture for Big Data. In SIGMOD, 2014.
45. S. Sridharan and J. M. Patel. Profiling R on a Contemporary Processor. PVLDB, 8(2), 2014.
46. M. Stonebraker, P. Brown, D. Zhang, and J. Becla. SciDB: A Database Management System for Applications with Complex Analytics. Computing in Science and Engineering, 15(3), 2013.
47. V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, O. O'Malley, S. Radia, B. Reed, and E. Baldeschwieler. Apache Hadoop YARN: Yet Another Resource Negotiator. In SOCC, 2013.
48. L. Yu, Y. Shao, and B. Cui. Exploiting Matrix Dependency for Efficient Distributed Matrix Computation. In SIGMOD, 2015.
49. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In NSDI, 2012.
50. C. Zhang, A. Kumar, and C. Ré. Materialization Optimizations for Feature Selection Workloads. In SIGMOD, 2014.
51. C. Zhang and C. Re. DimmWitted: A Study of Main-Memory Statistical Analytics. PVLDB, 7(12), 2014.
52. Y. Zhang, H. Herodotou, and J. Yang. RIOT: I/O-Efficient Numerical Computing without SQL. In CIDR, 2009.
53. Z. Zhang, C. Li, Y. Tao, R. Yang, H. Tang, and J. Xu. Fuxi: a Fault-Tolerant Resource Management and Job Scheduling System at Internet Scale. PVLDB, 7(13), 2014.
54. D. C. Zilio, J. Rao, S. Lightstone, G. M. Lohman, A. J. Storm, C. Garcia-Arellano, and S. Fadden. DB2 Design Advisor: Integrated Automatic Physical Database Design. In VLDB, 2004.