[1]WANG Chunkai,ZHUANG Fuzhen,SHI Zhongzhi.System resource allocation for variable data streams[J].CAAI Transactions on Intelligent Systems,2019,14(6):1278-1285.[doi:10.11992/tis.201908011]
Copy

System resource allocation for variable data streams

References:
[1] 孙大为, 张广艳, 郑纬民. 大数据流式计算:关键技术及系统实例[J]. 软件学报, 2014, 25(4):839-862 SUN Dawei, ZHANG Guangyan, ZHENG Weimin. Big data stream computing:technologies and instances[J]. Journal of software, 2014, 25(4):839-862
[2] 崔星灿, 禹晓辉, 刘洋, 等. 分布式流处理技术综[J]. 计算机研究与发展, 2015, 52(2):318-332 CUI Xingcan, YU Xiaohui, LIU Yang, et al. Distributed stream processing:a survey[J]. Journal of computer research and development, 2015, 52(2):318-332
[3] 王春凯, 孟小峰. 分布式数据流关系查询技术研究[J]. 计算机学报, 2016, 39(1):80-96 WANG Chunkai, MENG Xiaofeng. Relational query techniques for distributed data stream:a survey[J]. Chinese journal of computers, 2016, 39(1):80-96
[4] TOSHNIWAL A, TANEJA S, SHUKLA A, et al. Storm@twitter[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. Snowbird, Utah, USA, 2014:147-156.
[5] ZHENG Yu, ZHANG Lizhu, XIE Xing, et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th International Conference on World Wide Web. Madrid, Spain, 2009:791-800.
[6] WANG Chunkai, MENG Xiaofeng, GUO Qi, et al. OrientStream:a framework for dynamic resource allocation in distributed data stream management systems[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Indianapolis, Indiana, USA, 2016:2281-2286.
[7] WANG Chunkai, MENG Xiaofeng, GUO Qi, et al. Automating characterization deployment in distributed data stream management systems[J]. IEEE transactions on knowledge and data engineering, 2017, 29(12):2669-2681.
[8] SAX M J, CASTELLANOS M, CHEN Qiming, et al. Aeolus:an optimizer for distributed intra-node-parallel streaming systems[C]//Proceedings of 2013 IEEE 29th International Conference on Data Engineering. Brisbane, Australia, 2013:1280-1283.
[9] FU T Z J, DING Jianbing, MA R T B, et al. DRS:dynamic resource scheduling for real-time analytics over fast streams[C]//Proceedings of 2015 IEEE 35th International Conference on Distributed Computing Systems. Columbus, OH, USA, 2015:411-420.
[10] BITRAN G R, MORABITO R. State-of-the-art survey:open queueing networks:optimization and performance evaluation models for discrete manufa cturing systems[J]. Production and operations management, 1996, 5(2):163-193.
[11] ANIELLO L, BALDONI R, QUERZONI L. Adaptive online scheduling in storm[C]//Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems. Arlington, Texas, USA, 2013:207-218.
[12] KHOSHKBARFOROUSHHA A, RANJAN R, GAIRE R, et al. Resource usage estimation of data stream processing workloads in datacenter clouds[J]. arXiv:1501.07020, 2015.
[13] BISHOP C M. Mixture density networks[R]. Birmingham, UK:Aston University, 1994.
[14] POGGI N, CARRERA D, CALL A, et al. ALOJA:a systematic study of Hadoop deployment variables to enable automated characterization of cost-effectiveness[C]//Proceedings of 2014 IEEE International Conference on Big Data. Washington, DC, USA, 2014:905-913.
[15] Apache Hadoop[EB/OL].[2019-04-20]. http://hadoop.apache.org/.
[16] BERRAL J L, POGGI N, CARRERA D, et al. ALOJA-ML:a framework for automating characterization and knowledge discovery in hadoop deployments[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney, NSW, Australia, 2015:1701-1710.
[17] JAMSHIDI P, CASALE G. An uncertainty-aware approach to optimal configuration of stream processing systems[C]//Proceedings of 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. London, UK, 2016:39-48.
[18] VAN AKEN D, PAVLO A, GORDON G J, et al. Automatic database management system tuning through large-scale machine learning[C]//Proceedings of the 2017 ACM International Conference on Management of Data. Chicago, Illinois, USA, 2017:1009-1024.
[19] ABADI M, AGARWAL A, BARHAM P, et al. TensorFlow:large-scale machine learning on heterogeneous distributed systems[J]. arXiv:1603.04467, 2016.
[20] LI Jiexing, K?NIG A C, NARASAYYA V, et al. Robust estimation of resource consumption for SQL queries using statistical techniques[J]. Proceedings of the VLDB endowment, 2012, 5(11):1555-1566.
[21] AKDERE M, ?ETINTEMEL U, RIONDATO M, et al. Learning-based query performance modeling and prediction[C]//Proceedings of 2012 IEEE 28th International Conference on Data Engineering. Washington, DC, USA, 2012:390-401.
[22] Kafka[EB/OL].[2019-04-20]. http://kafka.apache.org/.
[23] SAX M J, CASTELLANOS M. Building a transparent batching layer for storm. HPL-2013-69[R]. Palo Alto, California, USA:HP Labs, 2014.
[24] JOHN G H, LANGLEY P. Estimating continuous distributions in Bayesian classifiers[C]//Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Montréal, Qué, Canada, 1995:338-345.
[25] HOEFFDING W. Probability inequalities for sums of bounded random variables[J]. Journal of the American statistical association, 1963, 58(301):13-30.
[26] OZA N C, RUSSELL S. Experimental comparisons of online and batch versions of bagging and boosting[C]//Proceedings of the Seventh ACM SIGKDD International conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2001:359-364.
[27] AHA D W, KIBLER D, ALBERT M K. Instance-based learning algorithms[J]. Machine learning, 1991, 6(1):37-66.
[28] HiBench[EB/OL].[2019-08-10]. https://github.com/intel-hadoop/HiBench/.
[29] TPC-H. TPC-H is a decision support benchmark[EB/OL].[2019-08-10]. http://www.tpc.org/tpch.
Similar References:

Memo

-

Last Update: 2019-12-25

Copyright © CAAI Transactions on Intelligent Systems