Skip to main content

A Distributed Computing Platform for Task Stream Processing

  • Conference paper
Information Computing and Applications (ICICA 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 391))

Included in the following conference series:

  • 1570 Accesses

Abstract

In order to process task stream, a distributed computing platform was put forward. The hardware of the platform includes one control terminal, multi computing nodes, one storage array and one fiber optic switch. The software of the platform includes DBMS, control software and tasks. The transaction process of the platform includes n (n ≥ 1) tasks, and each task belongs to a specific level. There are data dependences among the tasks, namely that the output data of the previous task is the input data of the following task. The transaction starts from the first level task, and then to the second, the third, …, at last to the nth level task. After the nth level task finished and output result data, the transaction comes to the end. The platform is applicable to the science computing that is characterized by a task stream composed of multi tasks, such as remote sensing data processing and complex electromagnetism analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhang, X., Li, H., Liu, Y.: The Research of Optimal Algorithm for Task Scheduling Underground Wireless Network Based on Distributed Computing. In: 2010 International Conference on Manufacturing Automation (ICMA), Hong Kong, pp. 151–155 (2010)

    Google Scholar 

  2. Jakovits, P., Srirama, S.N., Kromonov, I.: Stratus: A Distributed Computing Framework for Scientific Simulations on the Cloud. In: IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), Liverpool, pp. 1053–1059 (2012)

    Google Scholar 

  3. Xu, G., Lu, F., Yu, H., Xu, Z.: A Distributed Parallel Computing Environment for Bioinformatics Problems. In: Sixth International Conference on Grid and Cooperative Computing, Los Alamitos, CA, pp. 593–599 (2007)

    Google Scholar 

  4. Hawick, K.A., James, H.A., Maciunas, K.J., et al.: Geostationary-Satellite Imagery Applications on Distributed, High-Performance Computing. In: High Performance Computing on the Information Superhighway, Seoul, pp. 50–55 (1997)

    Google Scholar 

  5. Hifi, M., Saadi, T., Haddadou, N.: High Performance Peer-to-Peer Distributed Computing with Application to Constrained Two-Dimensional Guillotine Cutting Problem. In: 19th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, Ayia Napa, pp. 552–559 (2011)

    Google Scholar 

  6. Liu, H., Sorensen, S.-A., Nazir, A.: On-Line Automatic Resource Selection in Distributed Computing. In: IEEE International Conference on Cluster Computing and Workshops, New Orleans, LA, pp. 1–9 (2009)

    Google Scholar 

  7. Souza Ramos, D., Watershed, T.L.A.: Watershed: A High Performance Distributed Stream Processing System. In: 23rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Vitoria, Espirito Santo, pp. 191–198 (2011)

    Google Scholar 

  8. Chen, L., Agrawal, G.: Self-Adaptation in a Middleware for Processing Data Streams. In: 2004 International Conference on Autonomic Computing, pp. 292–293 (2004)

    Google Scholar 

  9. Slawinska, M., Slawinski, J., Sunderam, V.: Unibus: Aspects of Heterogeneity and Fault Tolerance in Cloud Computing. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (IPDPSW), Atlanta, GA, pp. 1–10 (2010)

    Google Scholar 

  10. Obaidat, M.S., Bedi, H., Bhandari, A., Bosco, M.S.D.: Design and Implementation of a Fault Tolerant Multiple Master Cloud Computing System. In: 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing (iThings/CPSCom), Dalian, pp. 82–88 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Weiyan, X., Wenqing, H., Dong, L., Youyi, D. (2013). A Distributed Computing Platform for Task Stream Processing. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-53932-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53931-2

  • Online ISBN: 978-3-642-53932-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics