Application-Level Caching Approach Based on Enhanced Aging Factor and Pearson Correlation Coefficient

Mulki Zulfa - Jenderal Soedirman University,Purbalingga, 53371, Indonesia
Sri Maryani - Jenderal Soedirman University,Purbalingga, 53371, Indonesia
- Ardiansyah - Ahmad Dahlan University, Bantul, 55191, Indonesia
Triyanna Widiyaningtyas - Malang State University, Malang, 65145, Indonesia
Waleed Ali - King Abdulaziz University, Rabigh, 25732, Saudi Arabia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.1.2143

Abstract


Relational database management systems (RDBMS) have long served as the fundamental infrastructure for web applications. Relatively slow access speeds characterize an RDBMS because its data is stored on a disk. This RDBMS weakness can be overcome using an in-memory database (IMDB). Each query result can be stored in the IMDB to accelerate future access. However, due to the limited capacity of the server cache in the IMDB, an appropriate data priority assessment mechanism needs to be developed. This paper presents a similar cache framework that considers four data vectors, namely the data size, timestamp, aging factor, and controller access statistics for each web page, which serve as the foundation elements for determining the replacement policy whenever there is a change in the content of the server cache. The proposed similarCache employs the Pearson correlation coefficient to quantify the similarity levels among the cached data in the server cache. The lowest Pearson correlation coefficients cached data are the first to be evicted from the memory. The proposed similarCache was empirically evaluated based on simulations conducted on four IRcache datasets. The simulation outcomes revealed that the data access patterns, and the configuration of the allocated memory cache significantly influenced the hit ratio performance. In particular, the simulations on the SV dataset with the most minor memory space configuration exhibited a 2.33% and 1% superiority over the SIZE and FIFO algorithms, respectively. Future tasks include building a cache that can adapt to data access patterns by determining the standard deviation. The proposed similarCache should raise the Pearson coefficient for often available data to the same level as most accessed data in exceptional cases.

Keywords


Application level caching; pearson correlation coefficient; web page; hit ratio

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References


T. Connolly and C. Begg, Database Systems : A Practical Approach to Design Implementation, and Management. Pearson, 2014.

A. Silberschatz, H. F. Korth, and S. Sudarshan, Database System Concepts. McGraw-Hill, 2010.

C. J. Date, An Introduction to Database Systems. Pearson, 2004.

M. Indrawan-santiago, “Database Research : Are We At A Crossroad ? Reflection on NoSQL,” in 15th International Conference on Network-Based Information Systems, 2012. doi: 10.1109/NBiS.2012.95.

G. Karnitis and G. Arnicans, “Migration of Relational Database to Document-Oriented Database: Structure Denormalization and Data Transformation,” in Proceedings - 7th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2015, 2015, pp. 113–118. doi: 10.1109/CICSyN.2015.30.

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.

W. Vogels, “Scaling Amazon ElastiCache for Redis with Online Cluster Resizing.”

I. Amazon Web Services, “Use Cases and How ElastiCache Can Help.”

K. Kaur, R. Rani, C. Sci, and E. Deptt, “Modeling and Querying Data in NoSQL Databases,” in IEEE International Conference on Big Data, 2013.

H. K. Lee, B. S. An, and E. J. Kim, “Adaptive Prefetching Scheme Using Web Log Mining in Cluster-based Web Systems,” 2009 IEEE Int. Conf. Web Serv., pp. 903–910, 2009, doi: 10.1109/ICWS.2009.127.

M. Kusuma, Widyawan, and R. Ferdiana, “Performance comparison of caching strategy on wordpress multisite,” in Proceeding - 2017 3rd International Conference on Science and Technology-Computer, ICST 2017, 2017, pp. 176–181. doi: 10.1109/ICSTC.2017.8011874.

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, pp. 8–10.

D. J. Carlson, Ebook Redis in Action. Manning Publications, 2013.

S. Bouchenak, A. Cox, S. Dropsho, S. Mittal, and W. Zwaenepoel, “Caching Dynamic Web Content: Designing and Analysing an Aspect-Oriented Solution,” in ACM/IFIP/USENIX 7th International Middleware Conference, 2006, pp. 1–21. doi: 10.1007/11925071_1.

Y. K. Alae El Alami, Mohamed Bahaj, “Supply of a Key Value Database Redis In-Memory by Data from a Relational Database,” in IEEE Mediterranean Electrotechnical Conference, IEEE, 2018, pp. 46–51.

A. E. Lotfy, A. I. Saleh, H. A. El-Ghareeb, and H. A. Ali, “A middle layer solution to support ACID properties for NoSQL databases,” J. King Saud Univ. - Comput. Inf. Sci., vol. 28, no. 1, pp. 133–145, 2016, doi: 10.1016/j.jksuci.2015.05.003.

J. Shamsi, M. A. Khojaye, and M. A. Qasmi, “Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions,” J. Grid Comput., vol. 11, no. 2, pp. 281–310, Jun. 2013, doi: 10.1007/s10723-013-9255-6.

J. Baker et al., “Megastore: Providing Scalable, Highly Available Storage for Interactive Services,” in Proceedings of the Conference on Innovative Data system Research (CIDR), 2011.

D. Akbari, B. Ali, and A. Ebrahimnejad, “A page replacement algorithm based on a fuzzy approach to improve cache memory performance,” Soft Comput., vol. 24, no. 2, pp. 955–963, 2020, doi: 10.1007/s00500-019-04624-w.

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.

W. Ali, S. M. Shamsuddin, and A. S. Ismail, “Intelligent Web proxy caching approaches based on machine learning techniques,” Decis. Support Syst., vol. 53, no. 3, pp. 565–579, Jun. 2012, doi: 10.1016/j.dss.2012.04.011.

R. Meloca and I. Nunes, “A comparative study of application-level caching recommendations at the method level,” Empir. Softw. Eng., vol. 27, no. 4, p. 88, Jul. 2022, doi: 10.1007/s10664-021-10089-z.

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

A. Blankstein, S. Sen, M. J. Freedman, A. Blankstein, S. Sen, and M. J. Freedman, “Hyperbolic Caching : Flexible Caching for Web Applications Hyperbolic Caching : Flexible Caching for Web Applications,” in Proceedings of the 2017 USENIX Annual Technical Conference, 2017.

J. Mertz and I. Nunes, “Understanding Application-Level Caching in Web Applications,” ACM Comput. Surv., vol. 50, no. 6, pp. 1–34, 2017, doi: 10.1145/3145813.

J. Mertz, I. Nunes, L. Della Toffola, M. Selakovic, and M. Pradel, “Satisfying Increasing Performance Requirements With Caching at the Application Level,” IEEE Softw., vol. 38, no. 3, pp. 87–95, May 2021, doi: 10.1109/MS.2020.3033508.

T. Ma, Y. Hao, W. Shen, Y. Tian, and M. Al-Rodhaan, “An Improved Web Cache Replacement Algorithm Based on Weighting and Cost,” IEEE Access, vol. 6, pp. 27010–27017, 2018, doi: 10.1109/ACCESS.2018.2829142.

T. Ma, J. Qu, W. Shen, Y. Tian, A. Al-Dhelaan, and M. Al-Rodhaan, “Weighted Greedy Dual Size Frequency Based Caching Replacement Algorithm,” IEEE Access, vol. 6, pp. 7214–7223, 2018, doi: 10.1109/ACCESS.2018.2790381.

J. Zhang, “Replacement Strategy of Web Cache Based on Data Mining,” Proc. - 2015 10th Int. Conf. P2P, Parallel, Grid, Cloud Internet Comput. 3PGCIC 2015, pp. 821–823, 2015, doi: 10.1109/3PGCIC.2015.75.

A. Blankstein, S. Sen, M. J. Freedman, A. Blankstein, S. Sen, and M. J. Freedman, “Hyperbolic Caching: Flexible Caching for Web Applications,” in Proceedings of the 2017 USENIX Annual Technical Conference, USENIX Annual Technical Conference, 2017. doi: 10.5555/3154690.3154738.

J. Mertz and I. Nunes, “A Qualitative Study of Application-Level Caching,” IEEE Trans. Softw. Eng., vol. 43, no. 9, pp. 798–816, 2017, doi: 10.1109/TSE.2016.2633992.

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.

J. Thomas, “Are ASEAN’s internet speeds world class?,” The Asean Post.

A. Saverimoutou, B. Mathieu, and S. Vaton, “Influence of internet protocols and CDN on web browsing,” in 2019 10th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2019 - Proceedings and Workshop, IEEE, 2019, pp. 1–5. doi: 10.1109/NTMS.2019.8763827.

D. Ayuba, A. Ismail, and M. Isa, “Evaluation of Page Response Time between Partial and Full Rendering in a Web-based Catalog System,” in Procedia Technology, Elsevier B.V., 2013, pp. 807–814. doi: 10.1016/j.protcy.2013.12.262.

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198.

S. Motaman, S. Ghosh, and N. Rathi, “Cache Bypassing and Checkpointing to Circumvent Data Security Attacks on STTRAM,” IEEE Trans. Emerg. Top. Comput., vol. 7, no. 2, pp. 262–270, Apr. 2019, doi: 10.1109/TETC.2017.2653813.

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.

W. Ali, S. M. Shamsuddin, and A. S. Ismail, “A Survey of Web Caching and Prefetching,” Int. J. Adv. Soft Comput. Appl., vol. 3, no. 1, pp. 1–27, 2011.

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.

T. Chen, “Obtaining the optimal cache document replacement policy for the caching system of an EC website,” Eur. J. Oper. Res., vol. 181, no. 2, pp. 828–841, 2007, doi: 10.1016/j.ejor.2006.05.034.

S. Podlipnig and L. Böszörmenyi, “A survey of Web cache replacement strategies,” ACM Comput. Surv., vol. 35, no. 4, pp. 374–398, Dec. 2003, doi: 10.1145/954339.954341.