Effective Information Retrieval Algorithm for Linear Multiprocessor Architecture

  • Zaki Ahmad Khan
  • Jamshed Siddiqui
  • Abdus Samad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 732)


Information retrieval is magnetizing important interest due to exponential development of the quantity of information accessible in different formats such as textual, numeric and image formats. A number of applications can be downloaded in parallel by several servers available on net using downloading applications. These applications may vary depending upon the mechanism used to improve to downloading time. One of the methods to speedup downloading is to incorporate concurrency. In this paper, a new approach for downloading files is proposed that uses a parallel architecture as a server. The server named as Linear-Crossed Cube (LCQ) is based on linear topology with all desirable topological properties. The load on the server balances dynamically. The proposed downloading algorithm is implemented, and downloading time is evaluated for number of queries. A comparative simulation study has been carried out along with the execution time to download file. The simulation results show significant improvement in the downloading time by using the proposed system.


Information retrieval Multiprocessor architecture Server Download Load balancing 


  1. 1.
    Canfora, G., Cerulo, L.: A taxonomy of information retrieval information models and tools. J. Comput. Inf. Technol. 12(3), 175–194 (2004)CrossRefGoogle Scholar
  2. 2.
    Song, H., Yin, Y., Chen, Y., Sun, X.H.: A cost-intelligent application-specific data layout scheme for parallel file systems. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, ACM, pp. 37–48 (2011)Google Scholar
  3. 3.
    Long, S., Zhao, Y., Chen, W., Tang, Y.: A prediction-based dynamic file assignment strategy for parallel file systems. Parallel Comput. 41, pp. 1–13 (2015)CrossRefGoogle Scholar
  4. 4.
    Cho, J.Y., Jin, H.W., Lee, M., Schwan, K.: Dynamic core affinity for high-performance file upload on hadoop distributed file system. Parallel Comput. 40, 722–737 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhu, Y., Yu, Y., Wang, W.Y., Tan, S.S., Low, T.C.: A balanced allocation strategy for file assignment in parallel i/o systems. In: 2010 IEEE Fifth International Conference on Networking, Architecture and Storage (NAS) IEEE, pp. 257–266 (2010)Google Scholar
  6. 6.
    Rao, G.N., Nagaraj, S.: Client level framework for parallel downloading of large file systems. Int. J. Comput. Appl. 3(2), 32–38 (2010)Google Scholar
  7. 7.
    Zeng, Z., Veeravalli, B.: Design and performance evaluation of queue-and-rate- adjustment dynamic load balancing policies for distributed networks. IEEE Trans. Comput. 55(11), 1410–1422 (2006)CrossRefGoogle Scholar
  8. 8.
    Khan, Z.A., Siddiqui, J., Samad, A.: Performance analysis of massively parallel architectures. BVICAM’s Int. J. Inf. Tech. 5(1), 563–568 (2013)Google Scholar
  9. 9.
    Samad, A., Rafiq, M.Q., Farooq, O.: LEC: an efficient scalable parallel interconnection network. In: Proceeding International Conference on Emerging Trends in Computer Science, Communication and Information Technology, pp. 453–458 (2010)Google Scholar
  10. 10.
    Tripathy, C.R.: Star-cube: a new fault tolerant interconnection topology for massively parallel systems. IE(I) J. ETE Div. 84(2), 83–92 (2004)Google Scholar
  11. 11.
    Adhikari, N., Tripathy, C.R.: On a new multicomputer interconnection topology for massively parallel systems. Int. J. Distrib. Parallel Sys. (IJDPS) 2(4) (2011)Google Scholar
  12. 12.
    Khan, Z.A., Siddiqui, J., Samad, A.: Linear crossed cube (LCQ): a new interconnection network topology for massively parallel system. Int. J. Comput. Netw. Inf. Secur. 7(3), 18–25 (2015)Google Scholar
  13. 13.
    Khan, Z.A., Siddiqui, J., Samad, A.: A novel multiprocessor architecture for massively parallel system. In Proceeding of IEEE International Conference on Parallel, Distributed and Grid Computing, pp. 466–471 2014.Google Scholar
  14. 14.
    Rodriguez, P., Biersack, E.: Dynamic Parallel access to replicated content in the internet. IEEE/ACM Trans. Network. 10(4) Aug 2002CrossRefGoogle Scholar
  15. 15.
    Khan, Z.A., Siddiqui, J., Samad, A.: A novel task scheduling algorithm for parallel system. In: 3rd IEEE International Conference for sustainable Global Development, pp. 3983–3986, New-Delhi, India (2016)Google Scholar
  16. 16.
    Zeitoun, A., Jamjoom, H., El-Gendy, M.: Scalable parallel-access for mirrored servers. In 20th IASTED International Conference on Applied Informatics (AI 2002), Innsbruck, Austria, Feb 2002Google Scholar
  17. 17.
    Kharwar, C., Viyas, T., Shah, V.: Content based parallel information retrieval for text files—exploiting the multiprocessor functionality. In Proceeding of International Conference of Emerging Research in Computing, Information, Communication and Applications (ERCICA-2014), Banglore 2014Google Scholar
  18. 18.
    Guan, N., Yai, W., Deng, Q., Gu, Z., Yu, G.: Schedulability analysis for non-preemptive fixed-priority multiprocessor scheduling. J. Syst. Archit. 57, 536–546 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zaki Ahmad Khan
    • 1
  • Jamshed Siddiqui
    • 1
  • Abdus Samad
    • 2
  1. 1.Department of Computer Science, Faculty of ScienceAligarh Muslim UniversityAligarhIndia
  2. 2.University Women’s Polytechnic, Aligarh Muslim UniversityAligarhIndia

Personalised recommendations