Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems
Tài liệu tham khảo
Afrati, 2016, Assignment problems of different-sized inputs in MapReduce, ACM Trans. Knowl. Discov. Data, 11
Armbrust, 2015, Spark SQL: relational data processing in Spark, 1383
Babu, 2010, Towards automatic optimization of MapReduce programs, 137
Bao, 2018, Learning-based automatic parameter tuning for big data analytics frameworks, 181
Bei, 2017, MEST: a model-driven efficient searching approach for MapReduce self-tuning, IEEE Access, 5, 3580, 10.1109/ACCESS.2017.2672675
Bei, 2016, RFHOC: a random-forest approach to auto-tuning Hadoop's configuration, IEEE Trans. Parallel Distrib. Syst., 27, 1470, 10.1109/TPDS.2015.2449299
Cai, 2017, A recommendation-based parameter tuning approach for Hadoop, 223
Chen, 2015, Machine learning-based configuration parameter tuning on Hadoop system, 386
Chen, 2014, A study of SQL-on-Hadoop systems, 154
Cherkasova, 2011, Performance modeling in MapReduce environments: challenges and opportunities, 5
Chiba, 2018, Towards selecting best combination of SQL-on-Hadoop systems and JVMs, 245
Deshpande, 2018, Automatic tuning of SQL-on-Hadoop engines on cloud platforms, 508
Ding, 2015, JellyFish: online performance tuning with adaptive configuration and elastic container in Hadoop Yarn, 831
Ead, 2014, PStorM: profile storage and matching for feedback-based tuning of MapReduce jobs, 1
Filho, 2019, Don't tune twice: reusing tuning setups for SQL-on-Hadoop queries, 93
Floratou, 2014, SQL-on-Hadoop: full circle back to shared-nothing database architectures, Proc. VLDB Endow., 7, 1295, 10.14778/2732977.2733002
Glushkova, 2019, MapReduce performance model for Hadoop 2.x, Inf. Syst., 79, 32, 10.1016/j.is.2017.11.006
Herodotou, 2011, Profiling, what-if analysis, and cost-based optimization of MapReduce programs, Proc. VLDB Endow., 4, 1111, 10.14778/3402707.3402746
Herodotou, 2013, A what-if engine for cost-based MapReduce optimization, IEEE Data Eng. Bull., 36, 5
Herodotou, 2020, A survey on automatic parameter tuning for big data processing systems, ACM Comput. Surv., 53, 1, 10.1145/3381027
Herodotou, 2011, Starfish: a self-tuning system for big data analytics, 261
Heudecker, 2015
Huai, 2014, Major technical advancements in Apache Hive, 1235
Jain, 2017, Analyzing & optimizing Hadoop performance, 116
Jiang, 2010, The performance of MapReduce: an in-depth study, Proc. VLDB Endow., 3, 472, 10.14778/1920841.1920903
Khaleel, 2018, Optimization of computing and networking resources of a Hadoop cluster based on software defined network, IEEE Access, 10.1109/ACCESS.2018.2876385
Khan
Kornacker, 2015, Impala: a modern, open-source SQL engine for Hadoop, 9
Kumar, 2017, Scalable performance tuning of Hadoop MapReduce: a noisy gradient approach, 375
Lee, 2016, Hadoop performance self-tuning using a fuzzy-prediction approach, 55
Lee, 2011, YSmart: yet another SQL-to-MapReduce translator, 25
Li, 2014, An adaptive auto-configuration tool for Hadoop, 69
Li, 2014, MRONLINE: MapReduce online performance tuning, 165
Liao, 2013, Gunther: search-based auto-tuning of MapReduce, 406
Lim, 2012, Stubby: a transformation-based optimizer for MapReduce workflows, Proc. VLDB Endow., 5, 1196, 10.14778/2350229.2350239
Liu, 2015, MR-COF: a genetic MapReduce configuration optimization framework, 344
Liu, 2012, Panacea: towards holistic optimization of MapReduce applications, 33
Mahgoub, 2020, OPTIMUSCLOUD: heterogeneous configuration optimization for distributed databases in the cloud, 189
Miner, 2012
Nykiel, 2010, MRShare: sharing across multiple queries in MapReduce, Proc. VLDB Endow., 3, 494, 10.14778/1920841.1920906
Poggi, 2016, The state of SQL-on-Hadoop in the cloud, 1432
Rajaraman, 2011
Sarma, 2013, Upper and lower bounds on the cost of a Map-Reduce computation, Proc. VLDB Endow., 6, 10.14778/2535570.2488334
Shi, 2014, MRTuner: a toolkit to enable holistic optimization for MapReduce jobs, Proc. VLDB Endow., 7, 1319, 10.14778/2733004.2733005
Shvachko, 2010, The Hadoop distributed file system, 1
Singhal, 2017, Performance assurance model for applications on SPARK platform, 131
Song, 2013, A Hadoop MapReduce performance prediction method, 820
The Apache Software Foundation
Thusoo, 2009, Hive: a warehousing solution over a Map-Reduce framework, Proc. VLDB Endow., 2, 1626, 10.14778/1687553.1687609
Van Aken, 2017, Automatic database management system tuning through large-scale machine learning, 1009
Wang, 2016, A novel method for tuning configuration parameters of Spark based on machine learning, 586
Wang, 2012, Predator — an experience guided configuration optimizer for Hadoop MapReduce, 419
Wu, 2013, A self-tuning system based on application profiling and performance analysis for optimizing Hadoop MapReduce cluster configuration, 89
Xin, 2013, Shark: SQL and rich analytics at scale, 13
Yigitbasi, 2013, Towards machine learning-based auto-tuning of mapreduce, 11
Zhang, 2016, Self-balancing job parallelism and throughput in Hadoop, 129
Zhang, 2013, AutoTune: optimizing execution concurrency and resource usage in MapReduce workflows, 175
Zhang, 2013, Benchmarking approach for designing a MapReduce performance model, 253