• Mulki Indana Zulfa Electrical Engineering Department, Engineering Faculty, Universitas Jenderal Soedirman, Indonesia
  • Muhammad Syaiful Aliim Electrical Engineering Department, Engineering Faculty, Universitas Jenderal Soedirman, Indonesia
  • Ari Fadli Computer Science Department, Mathematics and Natural Sciences Faculty, Universitas Gadjah Mada, Indonesia
  • Waleed Ali Information Technology Department, King Abdulaziz University, Saudi Arabia
Keywords: application level caching, in memory database, hit ratio, relational database, web application


Data access management in web-based applications that use relational databases must be well thought out because the data continues to grow every day. The Relational Database Management System (RDBMS) has a relatively slow access speed because the data is stored on disk. This causes problems with decreased database server performance and slow response times. One strategy to overcome this is to implement caching at the application level. This paper proposed SIMGD framework that models Application Level Caching (ALC) to speed up relational data access in web applications. The ALC strategy maps each controller and model that has access to the database into a node-data in the in-Memory Database (IMDB). Not all node-data can be included in IMDB due to limited capacity. Therefore, the SIMGD framework uses the Euclidean distance calculation method for each node-data with its top access data as a cache replacement policy. Node-data with Euclidean distance closer to their top access data have a high priority to be maintained in the caching server. Simulation results show at the 25KB cache configuration, the SIMGD framework excels in achieving hit ratios compared to the LRU algorithm of 6.46% and 6.01%, respectively.


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F. S. Rahayu, L. E. Nugroho, R. Ferdiana, and D. B. Setyohadi, “Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review,” Futur. Internet, vol. 12, no. 10, p. 174, Oct. 2020, doi: 10.3390/fi12100174.

M. D. Ariani, “Peran Kesepian dan Pengungkapan Diri Online Terhadap Kecanduan Internet pada Remaja Akhir,” UNISULA, 2018.

F. Sapty Rahayu, L. Kristiani, and S. Fuhrensia Wersemetawar, “Dampak Media Sosial terhadap Perilaku Sosial Remaja di Kabupaten Sleman, Yogyakarta,” in Seminar Nasional Inovasi Teknolog UN PGRI Kediri, 2019, vol. 2018, pp. 39–46, [Online]. Available:

M. Situmorang, “Measuring The Effectiveess of Consumer Dispute Resolution on Small Value E-Commerce Transaction,” J. Penelit. Huk. Jure, vol. 22, no. 4, p. 537, Dec. 2022, doi: 10.30641/dejure.2022.V22.537-550.

R. A. Wahab, “Comparative Analysis of Broadband Internet Development for Digital Economy in China and Indonesia,” J. Penelit. Pos dan Inform., vol. 9, no. 1, pp. 63–80, Oct. 2019, doi: 10.17933/jppi.v9i1.274.

Sathiyamoorthi V., Suresh P., Jayapandian N., Kanmani P., Deva Priya M., and S. Janakiraman, “An Intelligent Web Caching System for Improving the Performance of a Web-Based Information Retrieval System,” Int. J. Semant. Web Inf. Syst., vol. 16, no. 4, pp. 26–44, Oct. 2020, doi: 10.4018/IJSWIS.2020100102.

A. Gasparyan, “Most Important Metrics for Your Website Performance,” Monitis. 2019, Accessed: Oct. 26, 2019. [Online]. Available:

R. Ramakrishnan and A. Kaur, “Performance evaluation of web service response time probability distribution models for business process cycle time simulation,” J. Syst. Softw., vol. 161, p. 110480, Mar. 2020, doi: 10.1016/j.jss.2019.110480.

J. M. Medina, C. D. Barranco, and O. Pons, “Indexing techniques to improve the performance of necessity-based fuzzy queries using classical indexing of RDBMS,” in Fuzzy Sets and Systems, Nov. 2018, vol. 351, pp. 90–107, doi: 10.1016/j.fss.2017.09.008.

M. Luthfi, M. Data, and W. Yahya, “Perbandingan Performa Reverse Proxy Caching Nginx dan Varnish Pada Web Server Apache,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 4, pp. 1457–1463, 2018.

I. Amazon Web Services, “Use Cases and How ElastiCache Can Help,” 2019. (accessed Mar. 22, 2019).

N. Roy-Hubara, A. Sturm, and P. Shoval, “Designing NoSQL databases based on multiple requirement views,” Data Knowl. Eng., vol. 145, no. January, p. 102149, May 2023, doi: 10.1016/j.datak.2023.102149.

W. Puangsaijai and Sutheera Puntheeranurak, “A Comparative Study of Relational Database and Key-Value Database for Big Data Applications,” in International Electrical Engineering Congress, 2017, no. March, pp. 8–10.

M. I. Zulfa, R. Hartanto, and A. E. Permanasari, “Caching strategy for Web application – a systematic literature review,” Int. J. Web Inf. Syst., vol. 16, no. 5, pp. 545–569, Oct. 2020, doi: 10.1108/IJWIS-06-2020-0032.

S. Pendse et al., “Oracle Database In-Memory on Active Data Guard: Real-time Analytics on a Standby Database,” in 2020 IEEE 36th International Conference on Data Engineering (ICDE), Apr. 2020, vol. 2020-April, pp. 1570–1578, doi: 10.1109/ICDE48307.2020.00139.

G. Wu et al., “Cracking in-memory database index: A case study for Adaptive Radix Tree index,” Inf. Syst., vol. 104, p. 101913, Feb. 2022, doi: 10.1016/

C. Guerrero, I. Lera, and C. Juiz, “Performance improvement of web caching in Web 2.0 via knowledge discovery,” J. Syst. Softw., vol. 86, no. 12, pp. 2970–2980, 2013, doi: 10.1016/j.jss.2013.04.060.

NLANR, “The National Laboratory for Applied Network Research (NLANR),” 2001. (accessed Jul. 03, 2020).

X. Li, X. Wang, Z. Sheng, H. Zhou, and V. C. M. Leung, “Resource allocation for cache-enabled cloud-based small cell networks,” Comput. Commun., vol. 127, no. April, pp. 20–29, Sep. 2018, doi: 10.1016/j.comcom.2018.05.007.

Z. Zali, E. Aslanian, M. H. Manshaei, M. R. Hashemi, and T. Turletti, “Peer-Assisted Information-Centric Network (PICN): A Backward Compatible Solution,” IEEE Access, vol. 5, pp. 25005–25020, 2017, doi: 10.1109/ACCESS.2017.2762697.

H. Ibrahim, W. Yasin, N. I. Udzir, and B. Process, “Intelligent cooperative web caching policies for media objects based on J48 decision tree and Naïve Bayes supervised machine learning algorithms in structured peer-to-peer systems,” J. Inf. Commun. Technol., vol. 15, no. 2, pp. 85–116, 2016.

V. Holmqvist and J. Nilsfors, “Cachematic – Automatic Invalidation in Application-Level Caching Systems,” in International Conference on Performance Engineering, 2019, pp. 167–178.

J. Mertz and I. Nunes, “Automation of application-level caching in a seamless way,” Softw. Pract. Exp., vol. 48, no. 6, pp. 1218–1237, Jun. 2018, doi: 10.1002/spe.2571.

D. Zhang, Y. Liu, A. Liu, X. Mao, and Q. Li, “Efficient Path Query Processing Through Cloud-Based Mapping Services,” IEEE Access, vol. 5, pp. 12963–12973, 2017, doi: 10.1109/ACCESS.2017.2725308.

M. I. Zulfa, R. Hartanto, A. E. Permanasari, and W. Ali, “LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms,” Electronics, vol. 11, no. 19, p. 2978, Sep. 2022, doi: 10.3390/electronics11192978.

M. I. Zulfa, A. Fadli, A. E. Permanasari, and W. A. Ahmed, “Performance comparison of cache replacement algorithms onvarious internet traffic,” J. INFOTEL, vol. 15, no. 1, pp. 1–7, Feb. 2023, doi: 10.20895/infotel.v15i1.872.

How to Cite
M. I. Zulfa, Muhammad Syaiful Aliim, Ari Fadli, and Waleed Ali, “CACHE DATA REPLACEMENT POLICY BASED ON RECENTLY USED ACCESS DATA AND EUCLIDEAN DISTANCE”, J. Tek. Inform. (JUTIF), vol. 4, no. 4, pp. 875-881, Aug. 2023.