Multiple Agents for Data Processing

  • Ichiro SatohEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)


This paper proposes a distributed processing framework inspired from data processing. It unique among other data processing for large-scale data, socalled bigdata, because it can locally process data maintained in distributed nodes, including sensor or database nodes with non-powerful computing capabilities connected through low-bandwidth networks. It usesmobile agent technology as amechanism to distribute and execute data processing tasks to distributed nodes and aggregate their results. The paper outlines the architecture of the framework and evaluates its basic performance.


Mobile Agent Target Data Master Node Worker Agent Runtime System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Bu, Y., Howe, B., Balazinska, M., Ernst, M.D.: HaLoop: Efficient Iterative Data Processing on Large Clusters. Proceedings of the VLDB Endowment 3(1) (2010)Google Scholar
  2. 2.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Opearting Systems Design and Implementation, OSDI 2004 (2004)Google Scholar
  3. 3.
    Ekanayake, J., Li, H., Zhang, B., Gunarathne, T., Bae, S.H., Qiu, J., Fox, G.: Twister: a runtime for iterative MapReduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC 2010). ACM (2010)Google Scholar
  4. 4.
    Grossman, R., Gu, Y.: Data mining using high performance data clouds: experimental studies using sector and sphere. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 920–927. ACM (2008)Google Scholar
  5. 5.
    Jiang, W., Ravi, V.T., Agrawal, G.: A Map-Reduce System with an Alternate API for Multi-Core Environments. In: Proceedings of 10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (2010)Google Scholar
  6. 6.
    Talbot, J., Yoo, R.M., Kozyrakis, C.: Phoenix++: modular MapReduce for shared-memory systems. In: Proceedings of 2nd International Workshop on MapReduce and Its Applications (MapReduce 2011). ACM Press (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.National Institute of InformaticsChiyoda-kuJapan

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