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一种混合电力数据处理模型

A Hybrid Electric Power Data Processing Model
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摘要 提出了一种针对电力大数据处理的混合实时大数据处理的框架RTDP,提出了RTDP模型的四层体系结构,以及分层计算模式。考虑到RTDP系统对实时性和计算能力的要求,文章采用批量数据处理和流式数据实时处理两种方式相结合的混合处理方法。对于实时性要求不高的数据采用批量数据处理方式进行计算,同时将计算结果作为流式计算的先验知识。本文指出了未来RTDP系统的一般架构和计算方式,也是目前RTDP系统设计的一般方法。 This paper proposed a hybrid real-time big data processing( RTDP) architecture and then proposed the four layers of the architecture,and hierarchical computing model.Considered the high real-time and the powerful computing capabilities of the system,this paper hybrid the batch processing model and the streaming processing model,which use the batch processing result as the prior knowledge of the streaming processing.This paper points out the general framework for future RTDP system and calculation methods,which is also the general RTDP system design method currently.
作者 许元斌
出处 《自动化与仪器仪表》 2016年第10期210-213,共4页 Automation & Instrumentation
关键词 大数据 云计算 数据流 复杂事件处理 Big data cloud computing data stream CEP
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  • 1Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: IEEE Computer Society, 2010.97-104. [doi: 10.1109/SKG.2010.18].
  • 2Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaecapietra S, Teubner J, Kitsuregawa M, Leger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99-110. [doi: 10.1145/ 1739041.1739056].
  • 3Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299-310. [doi: 10.1145/1555349.1555384].
  • 4Hoefler T, Lumsdaine A, Dongarra J. Towards; efficient MapReduce using MPI. In: Oster P, ed. Proc. of the EuroPVM/MPI. Berlin: Springer-Verlag, 2009. 240-249. [doi: 10.100'7/978-3-642-03770-2_30].
  • 5Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2):494-505.
  • 6Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245-254. [doi: 10.1109/CLUSTER.2010.30].
  • 7Polo J, Carrera D, Becerra Y, Torres J, Ayguad6 E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the 1EEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373-380. [doi: 10.1109/NOMS.2010.5488494].
  • 8Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008.29-42.
  • 9Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In: Taufer M, Rfinger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1-9. [doi: 10.1109/IPDPSW.2010.5470880].
  • 10Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguad6 E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 653-662. [doi: 10.1109/ ICPP.2010.73].

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