Priority cache object replacement by using LRU, LFU, and FIFO algorithms to improve cache memory hit ratio

Authors

https://doi.org/10.22105/metaverse.v2i2.78

Abstract

Due to the increasing CPU and cache memory performance, using replacement algorithms is very important in various aspects of high-performance computing environments such as e-commerce systems, cache memory management in microprocessors, object management in operating systems, iteration strategies in information distribution systems, etc. Database server cache performance is an essential issue in e-commerce systems. Managing the entry and exit of cache objects with replacement algorithms can reduce the workload of the database server and improve server performance. The algorithms determine which objects remain in the cache memory and which go out to make room for new objects. In this way, the algorithms decrease user access time and enhance the system's performance. Most of these algorithms are developed by the famous LRU and LFU schemes and can fix their flaws; however, unlike them, they are challenging to implement. This research proposes a priority object replacement algorithm that is easy to implement. The PCORA algorithm is based on prioritizing cache memory objects according to three parameters. Experiments show that the proposed algorithm has a better hit ratio than others and can improve cache memory performance.         

Keywords:

Operating systems, Cache memory management, E-commerce systems, Replacement algorithms, Hit ratio, Performance

References

  1. [1] Haq, I. U., Khan, S., Khan, M., & Tamsal, S. (2024). Improving security and performance of databases through single cache system. 2024 4th international conference on blockchain technology and information security (ICBCTIS) (pp. 89–95). IEEE. https://doi.org/10.1109/ICBCTIS64495.2024.00022
  2. [2] Hasslinger, G., Okhovatzadeh, M., Ntougias, K., Hasslinger, F., & Hohlfeld, O. (2023). An overview of analysis methods and evaluation results for caching strategies. Computer networks, 228, 109583. https://doi.org/10.1016/j.comnet.2023.109583
  3. [3] Kushwah, J. S., & Tamrakar, S. (2018). An extensive review of webs caching techniques to reduce cache pollution. Imperial journal of interdisciplinary research, 6(13), 87–95. https://rntujournals.aisect.org/assets/upload_files/articles/3b39342f735878fb7ee90c670784dc89.pdf
  4. [4] Zulfa, M. I., Hartanto, R., & Permanasari, A. E. (2020). Caching strategy for Web application-a systematic literature review. International journal of web information systems, 16(5), 545–569. https://doi.org/10.1108/IJWIS-06-2020-0032
  5. [5] Wang, Y., Yang, Y., Han, C., Ye, L., Ke, Y., & Wang, Q. (2019). LR-LRU: A PACS-oriented intelligent cache replacement policy. IEEE access, 7, 58073–58084. https://doi.org/10.1109/ACCESS.2019.2913961
  6. [6] Liu, J., Wang, Y., Zhang, W., & Tian, K. (2023). A novel offloading and resource allocation scheme for time-critical tasks in heterogeneous internet of vehicles. 2023 2nd international conference for innovation in technology (INOCON) (pp. 1–7). IEEE. https://doi.org/10.1109/INOCON57975.2023.10101035
  7. [7] Wang, Y. L., Kim, K. T., Lee, B., & Youn, H. Y. (2018). A novel buffer management scheme based on particle swarm optimization for SSD. The journal of supercomputing, 74, 141–159. https://doi.org/10.1007/s11227-017-2119-2
  8. [8] Ooka, A., Eum, S., Ata, S., & Murata, M. (2018). Compact CAR: Low-overhead cache replacement policy for an ICN router. IEICE transactions on communications, 101(6), 1366–1378. https://doi.org/10.1587/transcom.2017EBP3299
  9. [9] Tailor, P. M., & Morena, R. D. (2017). A survey of database buffer cache management approaches. International journal of advanced research in computer science, 8(3), 409–414. https://doi.org/10.26483/ijarcs.v8i3.3026
  10. [10] Negrão, A. P., Roque, C., Ferreira, P., & Veiga, L. (2015). An adaptive semantics-aware replacement algorithm for web caching. Journal of internet services and applications, 6, 1–14. https://doi.org/10.1186/s13174-015-0018-4
  11. [11] Yang, P., Wang, Q., Ye, H., & Zhang, Z. (2019). Partially shared cache and adaptive replacement algorithm for NoC-based many-core systems. Journal of systems architecture, 98, 424–433. https://doi.org/10.1016/j.sysarc.2019.05.002
  12. [12] Samiee, K. (2009). A replacement algorithm based on weighting and ranking cache objects. International journal of hybrid information technology, 2(2), 93–104. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=6cfc4918a206ff0de7425d5574df22fe6a02786a
  13. [13] Akbari Bengar, D., Ebrahimnejad, A., Motameni, H., & Golsorkhtabaramiri, M. (2020). A page replacement algorithm based on a fuzzy approach to improve cache memory performance. Soft computing, 24(2), 955–963. https://doi.org/10.1007/s00500-019-04624-w
  14. [14] Akbari-Bengar, D., Ebrahimnejad, A., Motameni, H., & Golsorkhtabaramiri, M. (2020). Improving of cache memory performance based on a fuzzy clustering based page replacement algorithm by using four features. Journal of intelligent & fuzzy systems, 39(5), 7899–7908. https://doi.org/10.3233/JIFS-201360
  15. [15] Arya, G. P., Prasad, D., & Rana, S. S. (2018). An improved page replacement algorithm using block retrieval of pages. International journal of engineering & technology, 7(4.5), 32–35. https://doi.org/10.14419/IJET.V7I4.5.20004
  16. [16] Wu, H., Luo, Y., & Li, C. (2021). Optimization of heat-based cache replacement in edge computing system. The journal of supercomputing, 77(3), 2268–2301. https://doi.org/10.1007/s11227-020-03356-1
  17. [17] Do, C. T., Choi, H. J., Kim, J. M., & Kim, C. H. (2015). A new cache replacement algorithm for last-level caches by exploiting tag-distance correlation of cache lines. Microprocessors and microsystems, 39(4–5), 286–295. https://doi.org/10.1016/j.micpro.2015.05.005
  18. [18] Shin, D., Cho, K., & Bahn, H. (2020). File type and access pattern aware buffer cache management for rendering systems. Electronics, 9(1), 164. https://doi.org/10.3390/electronics9010164
  19. [19] Akbari Bengar, D., Jazayeri Rad, H., & Berenjian, G. (2012). An improvement in WRP block replacement policy with reviewing and solving its problems. Journal of advances in computer research, 3(4), 67-75. (In Persian). file:///C:/Users/NoteBook/OneDrive/Desktop/1035220120407.pdf
  20. [20] Anwar, U., Paik, J. Y., Jin, R., & Chung, T. S. (2017). Log-buffer aware cache replacement policy for flash storage devices. IEEE transactions on consumer electronics, 63(1), 77–84. https://doi.org/10.1109/TCE.2017.7931973
  21. [21] Jia, G., Han, G., Wang, H., & Wang, F. (2018). Cost aware cache replacement policy in shared last-level cache for hybrid memory based fog computing. Enterprise information systems, 12(4), 435–451. https://doi.org/10.1080/17517575.2017.1295321
  22. [22] Hajiakhondi-Meybodi, Z., Abouei, J., & Raouf, A. H. F. (2018). Cache replacement schemes based on adaptive time window for video on demand services in femtocell networks. IEEE transactions on mobile computing, 18(7), 1476–1487. https://doi.org/10.1109/TMC.2018.2864164
  23. [23] He, J., Jia, G., Han, G., Wang, H., & Yang, X. (2017). Locality-aware replacement algorithm in flash memory to optimize cloud computing for smart factory of industry 4.0. IEEE access, 5, 16252–16262. https://doi.org/10.1109/ACCESS.2017.2740327
  24. [24] Talaat, F. M., Ali, S. H., Saleh, A. I., & Ali, H. A. (2020). Effective cache replacement strategy (ECRS) for real-time fog computing environment. Cluster computing, 23(4), 3309–3333. https://doi.org/10.1007/s10586-020-03089-z
  25. [25] Jiang, L., & Zhang, X. (2020). Cache replacement strategy with limited service capacity in heterogeneous networks. IEEE access, 8, 25509–25520. https://doi.org/10.1109/ACCESS.2020.2970783
  26. [26] Sheu, J. P., & Chuo, Y. C. (2016). Wildcard rules caching and cache replacement algorithms in software-defined networking. IEEE transactions on network and service management, 13(1), 19–29. https://doi.org/10.1109/TNSM.2016.2530687
  27. [27] Kang, D. H., Han, S. J., Kim, Y. C., & Eom, Y. I. (2017). CLOCK-DNV: A write buffer algorithm for flash storage devices of consumer electronics. IEEE transactions on consumer electronics, 63(1), 85–91. https://doi.org/10.1109/TCE.2017.014700
  28. [28] Lee, M. C., Leu, F. Y., & Chen, Y. (2015). Pareto-based cache replacement for YouTube. World wide web, 18, 1523–1540. https://doi.org/10.1007/s11280-014-0318-9
  29. [29] Karami, A., & Guerrero-Zapata, M. (2015). An anfis-based cache replacement method for mitigating cache pollution attacks in named data networking. Computer networks, 80, 51–65. https://doi.org/10.1016/j.comnet.2015.01.020
  30. [30] Kim, S., Hwang, S. H., & Kwak, J. W. (2018). Adaptive-classification CLOCK: Page replacement policy based on read/write access pattern for hybrid DRAM and PCM main memory. Microprocessors and microsystems, 57, 65–75. https://doi.org/10.1016/j.micpro.2018.01.003
  31. [31] Ma, T., Qu, J., Shen, W., Tian, Y., Al-Dhelaan, A., & Al-Rodhaan, M. (2018). Weighted greedy dual size frequency based caching replacement algorithm. IEEE access, 6, 7214–7223. https://doi.org/10.1109/ACCESS.2018.2790381
  32. [32] Monazzah, A. M. H., Farbeh, H., & Miremadi, S. G. (2016). LER: Least-error-rate replacement algorithm for emerging STT-RAM caches. IEEE transactions on device and materials reliability, 16(2), 220–226. https://doi.org/10.1109/TDMR.2016.2562021
  33. [33] Motwani, A., Swain, D., Motwani, N., Vijan, V., Awari, A., & Dash, B. (2020). Enhancing multi-level cache performance using dynamic RF characteristics. Machine learning and information processing: proceedings of ICMLIP 2019 (pp. 277–286). Springer. https://doi.org/10.1007/978-981-15-1884-3_26
  34. [34] Nomura, H. (2020). Experimental investigation of lazy evaluation method in replacement algorithm for long-term re-reference cache management. Bulletin of networking, computing, systems, and software, 9(1), 83–90. http://bncss.org/index.php/bncss/article/viewFile/142/146
  35. [35] Olanrewaju, R. F., Al-Qudah, D. M. M., Azman, A. W., & Yaacob, M. (2016). Intelligent web proxy cache replacement algorithm based on adaptive weight ranking policy via dynamic aging. Indian journal of science and technology, 9(36), 1–7. http://dx.doi.org/10.17485/ijst/2016/v9i36/102159
  36. [36] Paulson, H., & Ramachandran, D. R. (2017). Page replacement algorithms--challenges and trends. International journal of computer & mathematical sciences IJCMS, 6(9), 112–116. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128697
  37. [37] Priya, B. K., Kumar, S., Begum, B. S., & Ramasubramanian, N. (2019). Cache lifetime enhancement technique using hybrid cache-replacement-policy. Microelectronics reliability, 97, 1–15. https://doi.org/10.1016/j.microrel.2019.03.011
  38. [38] Zhao, Y., Ma, T., Hao, Y., Shen, W., Tian, Y., & Al-Dhelaan, A. (2019). ICRA: Index based cache replacement algorithm for cloud storage. International journal of sensor networks, 29(1), 48–57. https://doi.org/10.1504/IJSNET.2019.097556
  39. [39] Yuan, Y., Shen, Y., Li, W., Yu, D., Yan, L., & Wang, Y. (2017). PR-LRU: A novel buffer replacement algorithm based on the probability of reference for flash memory. IEEE access, 5, 12626–12634. https://doi.org/10.1109/ACCESS.2017.2723758
  40. [40] Chen, X., Wang, Y., Xie, X., Hu, X., Basak, A., Liang, L., ... & Xie, Y. (2021). Rubik: A hierarchical architecture for efficient graph neural network training. IEEE transactions on computer-aided design of integrated circuits and systems, 41(4), 936–949. https://doi.org/10.1109/TCAD.2021.3079142
  41. [41] Alsharif, N. (2022). Fake opinion detection in an e-commerce business based on a long-short memory algorithm. Soft computing, 26(16), 7847–7854. https://doi.org/10.1007/s00500-022-06806-5
  42. [42] He, X., & Lin, M. (2022). Reliable auxiliary communication of UAV via relay cache optimization. Computer communications, 186, 33–44. https://doi.org/10.1016/j.comcom.2021.11.024
  43. [43] Akbari Bengar, D. (2025). Priority cache object replacement by using LRU, LFU and FIFO algorithms to improve cache memory hit ratio. Transactions on soft computing, 1(1), 1–13. https://doi.org/10.48314/tsc.v1i1.33

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Published

2025-06-15

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Vol. 2 No. 2 (2025): Metaversalize

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How to Cite

Akbari Bengar, D. . (2025). Priority cache object replacement by using LRU, LFU, and FIFO algorithms to improve cache memory hit ratio. Metaversalize, 2(2), 94-106. https://doi.org/10.22105/metaverse.v2i2.78
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