A Cloud-Native Serverless Architecture for Event-Driven, Low-Latency, and AI-Enabled Distributed Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P113Keywords:
Cloud-Native Architecture, Serverless Computing, Event-Driven Systems, Low-Latency Processing, AI-Enabled Distributed Systems, Function-As-A-Service (Faas), Intelligent Cloud SystemsAbstract
Cloud-native computing has reshaped the development of modern distributed systems by emphasizing scalability, resilience, and rapid innovation. Serverless architectures, built on Function-as-a-Service (FaaS) and managed cloud services, further advance this paradigm by abstracting infrastructure management and enabling fine-grained, event-driven execution. This paper presents a cloud-native serverless architecture designed to support event-driven processing, low-latency execution, and seamless integration of AI-enabled intelligence across distributed systems. The proposed architecture adopts an event-first design in which asynchronous events decouple system components, improving fault tolerance and enabling elastic scaling under highly variable workloads. Low-latency requirements are addressed through lightweight stateless functions, optimized event routing, multi-region deployment, and edge-aware execution strategies. To enable intelligent behavior, the architecture integrates AI and machine learning capabilities for event classification, intelligent routing, predictive scaling, and anomaly detection, implemented through serverless inference pipelines. A comprehensive performance evaluation demonstrates that the proposed approach achieves reduced operational overhead, improved resource utilization, and significant cost savings compared to traditional VM- and container-based deployments, while maintaining acceptable latency for real-time workloads. The results highlight both the benefits and trade-offs of serverless and event-driven designs, including cold-start effects and observability challenges. Overall, this work provides a practical reference architecture and design guidance for building next-generation distributed systems that combine cloud-native serverless computing, event-driven workflows, and AI-driven intelligence.
References
[1] Taibi, D., Lenarduzzi, V., & Pahl, C. (2018). Architectural patterns for microservices: A systematic mapping study. In Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018) (pp. 221–232). SciTePress. https://doi.org/10.5220/0006798302210232.
[2] Balalaie, A., Heydarnoori, A., & Jamshidi, P. (2015, September). Migrating to cloud-native architectures using microservices: an experience report. In European Conference on service-oriented and cloud computing (pp. 201-215). Cham: Springer International Publishing.
[3] Sewak, M., & Singh, S. (2018, April). Winning in the era of serverless computing and function as a service. In 2018 3rd International Conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE.
[4] Scheuner, J., & Leitner, P. (2020). Function as a Service performance evaluation: A multivocal literature review. Journal of Systems and Software, 170, 110708. https://doi.org/10.1016/j.jss.2020.110708.
[5] Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., … & Garraghan, P. (2019). Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118. https://doi.org/10.1016/j.iot.2019.100118.
[6] Scheuner, J., & Leitner, P. (2020). Function-as-a-Service performance evaluation: A multivocal literature review. arXiv. arXiv:2004.03276.
[7] Vayghan, L. A., Saied, M. A., Toeroe, M., & Khendek, F. (2019). Kubernetes as an availability manager for microservice applications. arXiv. arXiv:1901.04946.
[8] Al-Maamari, T. A. A. (2016). Aspects of event-driven cloud-native application development (Doctoral dissertation, Universitätsbibliothek der Universität Stuttgart).
[9] Lazzari, L., & Farias, K. (2021). An exploratory study on the effects of event-driven architecture on software modularity. arXiv. arXiv:2110.14699.
[10] Gilbert, J. (2018). Cloud Native Development Patterns and Best Practices: Practical architectural patterns for building modern, distributed cloud-native systems. Packt Publishing Ltd.
[11] David Chou, Event-Driven Serverless Architectures, online. https://dachou.github.io/2018/10/15/event-driven-serverless.html
[12] Venugopal, M. V. L. N., & Reddy, C. R. K. (2021). Serverless through cloud native architecture. Int. J. Eng. Res. Technol, 10, 484-496.
[13] Hassan, H. B., Barakat, S. A., & Sarhan, Q. I. (2021). Survey on serverless computing. Journal of Cloud Computing, 10, Article 39.
[14] Shukla, A., Chaturvedi, S., & Simmhan, Y. (2017). RIoTBench: A real-time IoT benchmark for distributed stream processing platforms. arXiv Preprint.
[15] Fan, C.-F., Jindal, A., & Gerndt, M. (2020). Microservices vs Serverless: A performance comparison on a cloud-native web application. Proceedings of the 2020 International Conference on Cloud Computing and Services Science.
[16] Shukla, A., Chaturvedi, S., & Simmhan, Y. (2017). RIoTBench: A real-time IoT benchmark for distributed stream processing platforms. arXiv Preprint. This work presents benchmarking and evaluation of distributed stream processing systems for real-time data pipelines foundational to event-driven real-time analytics.
[17] Chavan, A. (2021). Exploring event-driven architecture in microservices: Patterns, pitfalls, and best practices. International Journal of Science and Research Archive, 04(01), 229–249. Discusses event-driven microservices, associated patterns (e.g., publish-subscribe, event sourcing), and the complexities/challenges involved in implementation.
[18] Shafiei, H., Khonsari, A., & Mousavi, P. (2019). Serverless computing: A survey of opportunities, challenges and applications. arXiv. https://arxiv.org/abs/1911.01296
[19] Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., … Wu, C. (2021). Edge Cloud Polarization and Collaboration: A Comprehensive Survey for AI (Preprint). Surveys the collaborative interplay between cloud and edge AI paradigms, including real time processing and decision making challenges.
[20] Bhat, J., & Sundar, D. (2022). Building a Secure API-Driven Enterprise: A Blueprint for Modern Integrations in Higher Education. International Journal of Emerging Research in Engineering and Technology, 3(2), 123-134. https://doi.org/10.63282/3050-922X.IJERET-V3I2P113
[21] Bhat, J. (2022). The Role of Intelligent Data Engineering in Enterprise Digital Transformation. International Journal of AI, BigData, Computational and Management Studies, 3(4), 106-114. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P111
[22] Bhat, J., Sundar, D., & Jayaram, Y. (2022). Modernizing Legacy ERP Systems with AI and Machine Learning in the Public Sector. International Journal of Emerging Research in Engineering and Technology, 3(4), 104-114. https://doi.org/10.63282/3050-922X.IJERET-V3I4P112
[23] Sundar, D., & Jayaram, Y. (2022). Composable Digital Experience: Unifying ECM, WCM, and DXP through Headless Architecture. International Journal of Emerging Research in Engineering and Technology, 3(1), 127-135. https://doi.org/10.63282/3050-922X.IJERET-V3I1P113
[24] Sundar, D., Jayaram, Y., & Bhat, J. (2022). A Comprehensive Cloud Data Lakehouse Adoption Strategy for Scalable Enterprise Analytics. International Journal of Emerging Research in Engineering and Technology, 3(4), 92-103. https://doi.org/10.63282/3050-922X.IJERET-V3I4P111
[25] Sundar, D. (2022). Architectural Advancements for AI/ML-Driven TV Audience Analytics and Intelligent Viewership Characterization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 124-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P113
[26] Jayaram, Y., & Sundar, D. (2022). Enhanced Predictive Decision Models for Academia and Operations through Advanced Analytical Methodologies. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 113-122. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P113
[27] Jayaram, Y., Sundar, D., & Bhat, J. (2022). AI-Driven Content Intelligence in Higher Education: Transforming Institutional Knowledge Management. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 132-142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P115
[28] Jayaram, Y., & Bhat, J. (2022). Intelligent Forms Automation for Higher Ed: Streamlining Student Onboarding and Administrative Workflows. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 100-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P110
