CBR and Neural Networks Based Technique for Predictive Prefetching

  • Sohail Sarwar
  • Zia Ul-Qayyum
  • Owais Ahmed Malik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)


Cache prefetching in memory management greatly relies upon effectiveness of prediction mechanism to fully exploit available resources and for avoiding page faults. Plenty of techniques are available to devise strong prediction mechanism for prefetching but they either are situation specific (Locality of reference principle) or inadaptable (Markovian model) and costly. We have proposed a generic and adaptable technique benefiting from past experience by employing hybrid of Case Based Reasoning (CBR) and Neural Networks (NNs). Here we will be concerned with improving adaptation phase of CBR using NN and its impact on predictive accuracy for prefetching. The level of predictive accuracy attained (specifically in case adaptation of CBR) is ameliorated by handsome margin with declined cost than contemporary techniques as would be affirmed by results.


Prefetching Case Based Reasoning Neural Networks Locality of Reference Markovian Mode 


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  1. 1.
    Takahashi, H., Ahmad, H.F., Mori, K.: Layered Memory Architecture for High IO Intensive Information Services to Achieve Timeliness. In: HASE 2008 (2008)Google Scholar
  2. 2.
    Papathanasiou, A.E., Scott, M.L.: Aggressive Prefetching: An idea whose time has come. University of Rochester (2005),,scott
  3. 3.
    IO data Prefetching based on Sequential Stream Recognition,
  4. 4.
    Vardan, S.V.: Application of NN in predictive prefetching. K. R. Vaishnav Shanmugha Arts Science Technology and Research Academy (2005)Google Scholar
  5. 5.
    Mowry, T.C., Lam, M.S., Gupta, A.: Design and evaluation of a compiler algorithm for prefetching. In: Proc. of Fifth Int’l Conf. on Proceedings of the fifth international conference on Architectural support for programming languages and operating systems, pp. 62–73 (October 1992)Google Scholar
  6. 6.
    Whar, S.Y., Babka, O.: Neural Network Supported Adaptation in Case based Reasoning. In: Knowledge-Based Systems Centre, School of Computing, Information System and Mathematics, South Bank University, London, UK, GB, December 01, pp. 264–276 (2001)Google Scholar
  7. 7.
    Ukkonen, E.: On–line construction of suffix trees. In: Proc. Information Processing 92. IFIP Transactions A-12, vol. 1, pp. 484–492. Elsevier, Amsterdam (2005)Google Scholar
  8. 8.
    Ukkonen, E.: Constructing Suffix Trees On-Line in Linear Time. In: Leeuwen, J.v.(ed) Algorithms, Software, Architecture. Information Processing 1992, Proc. IFIP 12th World Computer Congress, Madrid, Spain, vol. 1, pp. 484–492. Elsevier Sci. Publ., Amsterdam (1992)Google Scholar
  9. 9.
    Khan, M.U., Ch, M.Q., Ahmad, H.F., Ali, L., Ali, A., Suguri, H.: Merging CBR and Neural Networks for SLA-Based Radio Resource Management for QoS Sensitive Cellular Networks. In: ISADS-ACM, pp. 263–269 (2007) ISBN:0-7695-2804-XGoogle Scholar
  10. 10.
    Sankar, K.P.: Foundations of Soft Case-based Reasoning. Indian Statistical Institute Simon c. K. Shiu Hong Kong Polytechnic University. John Wiley & Sons, Chichester (2004) ISBN:0-89791-187-3-XGoogle Scholar
  11. 11.
    Murray, K., Pesch, D.: Neural Network based Adaptive Radio Resource Management for GSM and IS136 Evolution. In: ISSC 2002, Cork, Ireland (June 2002)Google Scholar
  12. 12.
    Pan, S., Cherng, C., Dick, K., Ladner, R.E.: Algorithms to Take Advantage of Hardware Prefetching. In: Proceedings of the Nineteenth Annual ACM Symposium on Parallel Algorithms and ArchitecturesGoogle Scholar
  13. 13.
    Finnie, G., Sunt, Z.: Similarity and Metrics in Case-Based Reasoning. International Journal of Intelligent Systems 17, 273–287 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Wilke, W., Bergmann, R.: Techniques and knowledge used for Adaptation during Case-Based Problem Solving. In: Proceeding of 11th International Conference on Industrial and Engineering Applications of AI and ES (1998)Google Scholar
  15. 15.
    Keith, C.J., Van Rijsbergen: A new theoretical framework for information retrieval. In: Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval Palazzo, Pisa, Italy, pp. 194–200 (1986) ISBN:0-89791-187-3Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sohail Sarwar
    • 1
  • Zia Ul-Qayyum
    • 2
  • Owais Ahmed Malik
    • 1
  1. 1.School of Electrical Engineering and Computer SciencesNational University of Sciences and TechnologyIslamabadPakistan
  2. 2.University Institute of Information TechnologyUniversity of Arid AgricultureRawalpindiPakistan

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