Software Architecture Optimization Techniques for Enterprise CRM Performance Enhancement
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P115Keywords:
Enterprise CRM, Software Architecture Optimization, Microservices Architecture, Cloud Computing, Distributed Systems, Performance Engineering, Scalability, Database Optimization, Service-Oriented Architecture, Middleware Integration, API Gateway, Enterprise ApplicationsAbstract
However, traditional CRM architectures faced a major challenge in terms of operational complexity as data began to grow in complexity, workloads became more concurrent, integration requirements became overwhelming, infrastructure became a constraint, and scaling became an issue. These monolithic application designs, tightly coupled services, inefficient database operations, synchronous communication patterns, and limited resource elasticity were all a threat to the overall responsiveness and reliability of the system. As a result, enterprise organizations suffered from latency spikes, service downtime, varying user experiences, and inefficiencies in their infrastructure. Optimizing software architecture is therefore a crucial engineering approach of any enterprise CRM system today. Architectural optimization: Redesign of architectures to enhance the overall system efficiency with regard to structures, communication models, processing layers, infrastructure coordination mechanisms, and deployment strategies. Advanced architecture and concepts like microservices, service-oriented architecture (SOA), cloud-native computing, distributed caching and event-driven processing allow CRM systems to process large workloads with high availability, operational resilience and performance. There are a number of technological stages that can be identified in the evolution of CRM architectures. The traditional CRM systems used to be the client-server architectures with centralized databases. These systems offered very rudimentary customer management features, but failed to be scalable and flexible for integration. Later, web-based CRM platforms began to pop up, offering browser-based interfaces and enterprise integration. When enterprise workloads grew, the organizations resorted to the use of the service-oriented architecture model, to boost interoperability and modularity. SOA facilitated the reuse of services and standardized communication protocols leading to more efficient integration. But, an implementation of SOA has added in complexity and governance overhead for the middleware. Cloud computing, containerization, and microservices have revolutionized the way CRM is developed. Microservices based CRM systems allowed for independent deployment, autonomous scaling, distributed processing and continuous integration pipelines. The elasticity, resource optimization, and deployment automation were further boosted by cloud-native architectures.
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References
[1] Dragoni, N., Dustdar, S., Larsen, S. T., & Mazzara, M. (2017). Microservices: Migration of a mission critical system. arXiv preprint arXiv:1704.04173.
[2] Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A software architect's perspective. Addison-Wesley Professional.
[3] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, omega, and kubernetes. Communications of the ACM, 59(5), 50-57.
[4] Hohpe, G., & Woolf, B. (2004). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley Professional.
[5] Kleppmann, M. (2017). Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems. " O'Reilly Media, Inc.".
[6] Evans, E. (2004). Domain-driven design: tackling complexity in the heart of software. Addison-Wesley Professional.
[7] Richardson, C. (2018). Microservices patterns: with examples in Java. Simon and Schuster.
[8] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
[9] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., ... & Vogels, W. (2007). Dynamo: Amazon's highly available key-value store. ACM SIGOPS operating systems review, 41(6), 205-220.
[10] Newman, S. (2021). Building microservices: designing fine-grained systems. " O'Reilly Media, Inc.".
[11] Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., & Wilkes, J. (2015, April). Large-scale cluster management at Google with Borg. In Proceedings of the tenth european conference on computer systems (pp. 1-17).
[12] Pahl, C., & Jamshidi, P. (2016). Microservices: A Systematic Mapping Study. CLOSER (1), 137-146.
[13] Krafzig, D., Banke, K., & Slama, D. (2005). Enterprise SOA: service-oriented architecture best practices. Prentice Hall Professional.
[14] Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM) People, process and technology. Business process management journal, 9(5), 672-688.
[15] Xu, Y., Yen, D. C., Lin, B., & Chou, D. C. (2002). Adopting customer relationship management technology. Industrial management & data systems, 102(8), 442-452.
[16] Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., & Meedeniya, I. (2012). Software architecture optimization methods: A systematic literature review. IEEE Transactions on Software Engineering, 39(5), 658-683.
[17] Ruivo, P., Mestre, A., Johansson, B., & Oliveira, T. (2014). Defining the ERP and CRM integrative value. Procedia Technology, 16, 704–709. https://doi.org/10.1016/j.protcy.2014.10.019
[18] Alsultanny, Y. (2010). Database management and partitioning to improve database processing performance. Journal of Database Marketing & Customer Strategy Management, 17(3), 271-276.
[19] Da Xu, L. (2011). Enterprise systems: state-of-the-art and future trends. IEEE transactions on industrial informatics, 7(4), 630-640.
[20] Sweeney, R. (2010). Achieving service-oriented architecture: applying an enterprise architecture approach. John Wiley & Sons.
[21] Azadeh, A., Foroozan, H., Ashjari, B., Motevali Haghighi, S., Yazdanparast, R., Saberi, M., & Torki Nejad, M. (2017). Performance assessment and optimisation of a large information system by combined customer relationship management and resilience engineering: a mathematical programming approach. Enterprise Information Systems, 11(9), 1401-1415. IJERET-V1I4P108
