Redis Cache Optimization for Payment Gateways in the Cloud
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P104Keywords:
Redis Cache Optimization, Payment Gateways, Cloud Computing, Transaction Processing, Fault Tolerance, High AvailabilityAbstract
The adoption of cloud payment gateway solutions causes the need for reliable, quicker, easy, and efficient solutions to effectively large volumes of transactions in real-time. The payment gateway is one of the important modules that exist in modern electronic commerce to facilitate secure and real-time transactions between merchants and customers. However, challenges related to performance remain high, especially bottlenecks concerning response times and high-latency queries. It is at this point that Redis cache optimization bears the most value, especially where the application is running in a cloud infrastructure where computing resources must be closely managed to provide the best performance and stability. The final real-time data structure store that can be favored for high throughput is Redis, which is an open-source in-memory data structure store. Here are the roles it plays in cloud-based payment gateways, such as caching of frequently used data, reducing database access, and payment validation procedures. By saving payment information and validation steps, Redis improves general response time, having less burdening the central database, and all the payment services can run completely, even at peak hours. Redis caching for such uses not only enhances system performance but also reduces response time, enhances user satisfaction, and increases the possibility of a successful transaction. This paper specifically discusses the design and different strategies for Redis cache optimization in payment gateways in cloud environments. Some of these techniques include partitioning, replication, and eviction policies on data which are important for increasing reliability and the rate of the payment systems in the cloud. For instance, it solves real-time consistencies, fault tolerance, scalability issues, and the problem with data persistence in systems that use in-memory data stores such as Redis. Using evaluative data and benchmarking, this work identifies the effects of Redis caching in terms of transaction processing and overall system utilization. In the microservices-based web applications involving multiple payment gateways, discussion extends to cloud deployment platforms like AWS, Google Cloud, and Microsoft Azure and how Amazon ElastiCache and Google Memorystore as native services employ Redis for payment processing. Cache-aside, write-through, and read-through caching use cases are explained, and parameters to fine-tune Redis for achieving a low memory footprint with high hit ratios are also suggested. Finally, this paper proposes a detailed evaluation of Redis cache optimization within cloud-based payment gates, including design patterns and procedures that are likely to yield high returns for the adoption of Redis cache as an important element in the development of payment gateways
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References
[1] Gulati, V. P., & Srivastava, S. (2007). The empowered internet payment gateway. In International Conference on E-Governance (pp. 98-107).
[2] Nelson, J. (2016). Mastering redis. Packt Publishing Ltd.
[3] Ji, Z., Ganchev, I., O’Droma, M., & Ding, T. (2014, October). A distributed Redis framework for use in the UCWW. In 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (pp. 241-244). IEEE.
[4] Qiu, H. J., Liu, S., Song, X., Khan, S., & Pekhimenko, G. (2022, October). Pavise: Integrating Fault Tolerance Support for Persistent Memory Applications. In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (pp. 109-123).
[5] Lee, T., Kim, Y., & Hwang, E. (2018, January). Abnormal payment transaction detection scheme based on scalable architecture and redis cluster. In 2018 International Conference on Platform Technology and Service (PlatCon) (pp. 1-6). IEEE.
[6] Tewari, H., & O’Mahony, D. (2003). Real-time payments for mobile IP. IEEE Communications Magazine, 41(2), 126-136.
[7] Kumar, A. (2022). Using Redis for persistent storage in serverless architecture to maintain state management (Doctoral dissertation, Dublin, National College of Ireland).
[8] Ganesan, A., Alagappan, R., Arpaci-Dusseau, A. C., & Arpaci-Dusseau, R. H. (2017). Redundancy does not imply fault tolerance: Analysis of distributed storage reactions to file-system faults. ACM Transactions on Storage (TOS), 13(3), 1-33.
[9] Costa, C. H., Maia, P. H. M., & Carlos, F. (2015, April). Sharding by hash partitioning. In Proceedings of the 17th International Conference on Enterprise Information Systems (Vol. 1, pp. 313-320).
[10] Venkateswaran, N., & Changder, S. (2017, November). Simplified data partitioning in a consistent hashing based sharding implementation. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 895-900). IEEE.
[11] Bhatia, S., Varki, E., & Merchant, A. (2010, August). Sequential prefetch cache sizing for maximal hit rate. In 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (pp. 89-98). IEEE.
[12] Dan, A., & Sitaram, D. (1997). Multimedia caching strategies for heterogeneous application and server environments. Multimedia Tools and Applications, 4, 279-312.
[13] Ding, W., Kandemir, M., Guttman, D., Jog, A., Das, C. R., & Yedlapalli, P. (2014, August). Trading cache hit rate for memory performance. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation (pp. 357-368).
[14] GHOFAR, A. L., PUTRA, R. N. P., & HAMIDAH, S. N. (2022). Implementation Of Gateway Technology (Go-Pay) In Increasing Transaction Efficiency In MSMEs Dapur Restu. Journal of Information Systems, Digitization and Business, 1(1), 08-14.
[15] Milkau, U. (2019). International payments: Current alternatives and their drivers. Journal of Payments Strategy & Systems, 13(3), 201-216.
[16] Lee, D., Choi, J., Kim, J. H., Noh, S. H., Min, S. L., Cho, Y., & Kim, C. S. (1999, May). On the existence of a spectrum of policies that subsumes the least recently used (LRU) and least frequently used (LFU) policies. In Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of Computer Systems (pp. 134-143).
[17] Yang, J. H., & Lin, P. Y. (2016). A mobile payment mechanism with anonymity for cloud computing. Journal of Systems and Software, 116, 69-74.
[18] Daruvuri, R., Patibandla, K.(2023). Enhancing Data Security and Privacy in Edge Computing: A Comprehensive Review of Key Technologies and Future Directions. International Journal of Research In Electronics And Computer Engineering, 11(1), pp. 77-88.
[19] Seifelnasr, M., Nakkar, M., Youssef, A., & AlTawy, R. (2020, November). A lightweight authentication and inter-cloud payment protocol for edge computing. In 2020 IEEE 9th International Conference on Cloud Networking (CloudNet) (pp. 1-4). IEEE.
[20] Ayyoub, B., Zahran, B., Nisirat, M. A., Al-Taweel, F. M., & Al Khawaldah, M. (2021). A proposed cloud-based billers hub using secured e-payments system. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(1), 339-348.
