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IJMLC 2021 Vol.11(3): 250-255 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.3.1043

An efficient Intelligent Cache Replacement Policy Suitable for PACS

Yinyin Wang, Yuwang Yang, and Qingguang Wang

Abstract—An efficient intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the Support vector machine (SVM) with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called SVM-LRU. The SVM-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented SVM-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The SVM algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the SVM-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE and GDS.

Index Terms—PACS, cache replacement policy, SVM, hybrid Storage.

Yinyin Wang and Yuwang Yang are with the School of Computer Science and Engineering, Nanjing University of Science and Technology, China (e-mail: wyywx699@163.com, yuwangyang@njust.edu.cn).
Qingguang Wang is with the Yancheng 1st People’s Hospital and Medical School of Nantong University, China (e-mail: wqg699@163.com).


Cite: Yinyin Wang, Yuwang Yang, and Qingguang Wang, "An efficient Intelligent Cache Replacement Policy Suitable for PACS," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 250-255, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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