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Stock Cloud Computing Platform:Architecture and Prototype Systems

  • Jianfeng Wu
  • Jing Sun
  • Xue Bai
  • Li Xue
  • Lili Lin
  • Xiongxiong Zhang
  • Shuo Bai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)

Abstract

The Challenge of Big Data leads to a great interests in Cloud Computing in both research and industry. Many Cloud Platforms have been proposed and implemented in various applications, however, there are few works focusing on designing integrated platforms from a data driven perspective for stock market. In this paper, we propose a cloud platform for stock market with four-tier architectures that introduces Infrastructure as a Service(IaaS),Data as a Service(DaaS),Platform as a Service(PaaS) and Software as a Service(SaaS). The proposed cloud platform integrates big data processing, data mining, and cloud computing technologies, and can provide high-performance computing and s data service as well. Finally, a case study on market surveillance in stock market validates the effectiveness and efficiency of the proposed prototype systems.

Keywords

big data stock cloud computing platform architecture 

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References

  1. 1.
    Youseff, L., Butrico, M., Da Silva, D.: Toward a Unified Ontology of Cloud Computing. In: Grid Computing Environments Workshop, pp. 1–10 (2008)Google Scholar
  2. 2.
    Chen, K., Zheng, W.M.: Cloud computing:System instances and current research. Journal of Software 20(5), 1337–1348 (2009)CrossRefGoogle Scholar
  3. 3.
    Raj, H., Nathuji, R., Singh, A.: England p. Resource management for isolation enhanced cloud services. In: Sion, R. (ed.) Proc. of the 2009 ACM Workshop on Cloud Computing Security,CCSW 2009,Co-Located with the 16th ACM Computer and Communications Security Conf., CCS 2009, pp. 77–84. Association for Computing Machinery, New York (2009)Google Scholar
  4. 4.
    Essvale Corporation Limited, Bizle Professional Series,Business Knowledge For It. In: Trading And ExchangesGoogle Scholar
  5. 5.
  6. 6.
    York, J.: Banking Technology Forecast is Partly Cloudy(2010), http://www.cloudbulls.com/banking-technologyforecast-is-partly-cloudy.2010.8
  7. 7.
    Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D.J., Silberschatz, A., Rasin, A.: HadoopDB:An architectural hybrid of MapReduce and DBMS technologies for analytical workloads. PVLDB, 922–933 (2009)Google Scholar
  8. 8.
    Ahrens, M., Alonso, G.: Relational databases,virtualization,and the cloud. In: Abiteboul, S., Böhm, K., Koch, C., Tan, K.L. (eds.) Proc. of the 27th Int’l Conf. on Data Engineering (ICDE 2011), p. 1254. IEEE Computer Society Press, New York (2011)CrossRefGoogle Scholar
  9. 9.
    Garfinkel, S.L.: An evaluation of Amazon’s grid computing services: EC2,S3 and SQS, TR-08-07. Harvard University, Cambridge (2007)Google Scholar
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Eucalyptus project (EB /OL) (December 26, 2008), http://eucalyptuscs.ucsb.edu
  14. 14.
    Nurmid, Wolskir, Grzegdrczykc, et al.: The Eucalyptus Open-source cloud computing system. In: Proc of Workshop on Cloud Computing and its Applications (2008)Google Scholar
  15. 15.
  16. 16.
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jianfeng Wu
    • 1
  • Jing Sun
    • 2
  • Xue Bai
    • 2
  • Li Xue
    • 2
  • Lili Lin
    • 1
  • Xiongxiong Zhang
    • 1
  • Shuo Bai
    • 1
  1. 1.Shanghai Stock ExchangeShanghaiP.R. China
  2. 2.School of Computer ScienceFudan UniversityShanghaiP.R. China

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