Comparative Performance and Optimization Strategies for Cloud-Native Architectures: A Focus on Scalability, Cost, and Resource Utilization

PDF (1327KB), PP.87-102

Views: 0 Downloads: 0

Author(s)

Gift Aruchi Nwatuzie 1,*

1. Department of Computer Systems Engineering, School of Architecture, Computing and Engineering, University of East London, Docklands Campus, E16 2RD, London, United Kingdom

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2026.03.06

Received: 20 Mar. 2025 / Revised: 24 Apr. 2025 / Accepted: 8 Jan. 2026 / Published: 8 Jun. 2026

Index Terms

Cloud-Native Architectures, Performance, Optimization Strategies, Scalability, Cost, Resource Utilization

Abstract

Cloud-native architectures have become essential for modern application development, offering scalability, flexibility, and cost efficiency through paradigms like microservices, serverless computing, and event-driven systems. However, performance trade-offs, resource underutilization, and operational inefficiencies persist across different architectural models. This study delivers a comparative performance evaluation of four leading cloud-native architectures—Service Mesh, Event-Driven Systems, Serverless Computing, and Polyglot Persistence across AWS and GCP platforms. Using a controlled experimental setup, key performance metrics including response time, throughput, resource utilization, and operational cost (OC) were assessed under varying workloads. Serverless computing demonstrated superior cost-efficiency and dynamic scaling, though hampered by cold-start delays, while event-driven systems struck a balance between responsiveness and cost. Optimization strategies such as cold-start mitigation, adaptive auto-scaling, and hybrid storage improvements yielded significant performance gains across all architectures. The research provides critical insights for developers and system architects, offering data-driven recommendations to guide architectural choices and optimize cloud-native deployments. The study’s significance lies in its empirical approach, bridging theoretical design with real-world implementation to advance best practices in building scalable and sustainable cloud-native applications.

Cite This Paper

Gift Aruchi Nwatuzie, "Comparative Performance and Optimization Strategies for Cloud-Native Architectures: A Focus on Scalability, Cost, and Resource Utilization", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.3, pp. 87-102, 2026. DOI:10.5815/ijieeb.2026.03.06

Reference

[1]Pahl, C., & Jamshidi, P. (2022). Microservices and containers. Journal of Systems and Software, 193, 111386. https://doi.org/10.1016/j.jss.2022.111386
[2]Gannon, D., Barga, R., & Sundaresan, N. (2021). Cloud-native applications and microservices: Opportunities and challenges. IEEE Internet Computing, 25(3), 18–24. https://doi.org/10.1109/MIC.2021.3077012
[3]Sharma, N., Choudhary, A., & Saini, H. (2023). Cloud-native computing: A comprehensive review of current trends, challenges, and future research directions. Sustainable Computing: Informatics and Systems, 38, 100905. https://doi.org/10.1016/j.suscom.2023.100905
[4]Khan, M. M., Awan, I. U., & Yasin, M. (2021). Serverless computing: Applications, challenges, and future trends. Journal of Cloud Computing, 10(1), 1–28. https://doi.org/10.1186/s13677-021-00253-9
[5]Al-Debagy, O., & Martinek, P. (2022). A review of microservices architecture: Challenges and solutions. Journal of Cloud Computing, 11(1), 1–29. https://doi.org/10.1186/s13677-022-00307-7
[6]Ayoola, V. B., Audu, B. A., Boms, J. C., Ifoga, S. M., Mbanugo, O. J., & Ugochukwu, U. N. (2024). Integrating industrial hygiene in hospice and home-based palliative care to enhance quality of life for respiratory and immunocompromised patients. IRE Journals, 8(5).
[7]Ayoola, V. B., Idoko, P. I., Eromonsei, S. O., Afolabi, O., Apampa, A. R., & Oyebanji, O. S. (2024). The role of big data and AI in enhancing biodiversity conservation and resource management in the USA. World Journal of Advanced Research and Reviews, 23(2), 1851–1873. https://doi.org/10.30574/wjarr.2024.23.2.2350
[8]Ayoola, V. B., Ugochukwu, U. N., Adeleke, I., Michael, C. I., Adewoye, M. B., & Adeyeye, Y. (2024). Generative AI-driven fraud detection in health care enhancing data loss prevention and cybersecurity analytics for real-time protection of patient records. International Journal of Scientific Research and Modern Technology (IJSRMT), 3(11). https://www.ijsrmt.com/index.php/ijsrmt/article/view 
[9]Enyejo, J. O., Babalola, I. N. O., Owolabi, F. R. A. Adeyemi, A. F., Osam-Nunoo, G., & Ogwuche, A. O. (2024). Data-driven digital marketing and battery supply chain optimization in the battery powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects. International Journal of Scholarly Research and Reviews, 2024, 05(02), 001–020. https://doi.org/10.56781/ijsrr.2024.5.2.0045 
[10]Idoko, I. P., Igbede, M. A., Manuel, H. N. N., Ijiga, A. C., Akpa, F. A., & Ukaegbu, C. (2024). Assessing the impact of wheat varieties and processing methods on diabetes risk: A systematic review. World Journal of Biology Pharmacy and Health Sciences, 18(2), 260–277. https://wjbphs.com/sites/default/files/WJBPHS-2024-0286.pdf
[11]Idoko, I. P., Ijiga, O. M., Agbo, D. O., Abutu, E. P., Ezebuka, C. I., & Umama, E. E. (2024). Comparative analysis of Internet of Things (IoT) implementation: A case study of Ghana and the USA – vision, architectural elements, and future directions. World Journal of Advanced Engineering Technology and Sciences, 11(1), 180–199.
[12]Idoko, I. P., Ijiga, O. M., Akoh, O., Agbo, D. O., Ugbane, S. I., & Umama, E. E. (2024). Empowering sustainable power generation: The vital role of power electronics in California's renewable energy transformation. World Journal of Advanced Engineering Technology and Sciences, 11(1), 274–293.
[13]Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Akoh, O., & Ileanaju, S. (2024). Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression.
[14]Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Ugbane, S. I., Akoh, O., & Odeyemi, M. O. (2024). Exploring the potential of Elon Musk's proposed quantum AI: A comprehensive analysis and implications. Global Journal of Engineering and Technology Advances, 18(3), 48–65.
[15]Idoko, I. P., Ijiga, O. M., Harry, K. D., Ezebuka, C. C., Ukatu, I. E., & Peace, A. E. (2024). Renewable energy policies: A comparative analysis of Nigeria and the USA.
[16]Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 7(1), 48–63. https://ijbpsa.com/sites/default/files/IJBPSA-2024-0015.pdf
[17]Ijiga, A. C., Abutu, E. P., Idoko, P. I., Ezebuka, C. I., Harry, K. D., Ukatu, I. E., & Agbo, D. O. (2024). Technological innovations in mitigating winter health challenges in New York City, USA. International Journal of Science and Research Archive, 11(1), 535–551. https://ijsra.net/sites/default/files/IJSRA-2024-0078.pdf
[18]Ijiga, A. C., Aboi, E. J., Idoko, P. I., Enyejo, L. A., & Odeyemi, M. O. (2024). Collaborative innovations in artificial intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking. Global Journal of Engineering and Technology Advances, 18(3), 106–123. https://gjeta.com/sites/default/files/GJETA-2024-0046.pdf
[19]Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. Open Access Research Journals, 13(1). https://doi.org/10.53022/oarjst.2024.11.1.0060
[20]Jamshidi, P., Pahl, C., Mendonça, N. C., Lewis, J., & Tilkov, S. (2021). Microservices: The journey so far and challenges ahead. IEEE Software, 38(1), 24–35. https://doi.org/10.1109/MS.2020.2973362
[21]Castro, P., Ishakian, V., Muthusamy, V., & Slominski, A. (2021). The rise of serverless computing. Communications of the ACM, 64(1), 42–50. https://doi.org/10.1145/3429886
[22]Li, Z., Xu, L., Chen, Y., Xu, J., & Huang, X. (2022). An empirical study on the performance of event-driven microservices in cloud-native environments. Journal of Systems and Software, 186, 111209. https://doi.org/10.1016/j.jss.2021.111209
[23]Enyejo, J. O., Fajana, O. P., Jok, I. S., Ihejirika, C. J.,  Awotiwon,  B. O., & Olola, T. M. (2024). Digital Twin Technology, Predictive Analytics, and Sustainable Project Management in Global Supply Chains for Risk Mitigation, Optimization, and Carbon Footprint Reduction through Green Initiatives. International Journal of Innovative Science and Research Technology, Volume 9, Issue 11, November– 2024.  ISSN No:-2456-2165.   https://doi.org/10.38124/ijisrt/IJISRT24NOV1344
[24]Enyejo, L. A., Adewoye, M. B., & Ugochukwu, U. N. (2024). Interpreting federated learning (FL) models on edge devices by enhancing model explainability with computational geometry and advanced database architectures. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6). https://doi.org/10.32628/CSEIT24106185
[25]eG Innovations. (2022, June 27). The what and the why of cloud-native applications: An introductory guide. eG Innovations. https://www.eginnovations.com/blog/the-what-and-the-why-of-cloud-native-applications-an-introductory-guide/
[26]Zhang, W., Li, K., Zhang, L., & Wu, C. (2020). Cost-efficient autoscaling for containerized cloud applications. Future Generation Computer Systems, 110, 1042–1053. https://doi.org/10.1016/j.future.2019.09.016
[27]Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., ... & Slominski, A. (2017). Serverless computing: Current trends and open problems. Research Advances in Cloud Computing, 1–20. https://doi.org/10.1007/978-981-10-5026-8_1
[28]Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C. C., Khandelwal, A., Pu, Q., ... & Stoica, I. (2019). Cloud programming simplified: A Berkeley view on serverless computing. arXiv preprint arXiv:1902.03383. https://arxiv.org/abs/1902.03383
[29]Alonso, J., Orue-Echevarria, L., Casola, V., Torre, A. I., Huarte, M., Osaba, E., & Lobo, J. L. (2023). Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review. Journal of Cloud Computing, 12(1), 6.
[30]Yu, L., Liu, Y., Wu, L., Wang, Z., & Zhang, C. (2020). A measurement study of resource utilization in cloud-native applications. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2020.2997330
[31]Zhao, Z., Zhou, F., Chen, Q., & Ma, J. (2020). Distributed message queues: A survey of technologies, architectures, and challenges. IEEE Access, 8, 183676–183695. https://doi.org/10.1109/ACCESS.2020.3028687
[32]Adzic, G., & Chatley, R. (2017). Serverless computing: Economic and architectural impact. Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 884–889. https://doi.org/10.1145/3106237.3117767
[33]Manuel, H. N. N., Adeoye, T. O., Idoko, I. P., Akpa, F. A., Ijiga, O. M., & Igbede, M. A. (2024). Optimizing passive solar design in Texas green buildings by integrating sustainable architectural features for maximum energy efficiency. Magna Scientia Advanced Research and Reviews, 11(1), 235–261. https://doi.org/10.30574/msarr.2024.11.1.0089
[34]Onuh, J. E., Idoko, I. P., Igbede, M. A., Olajide, F. I., Ukaegbu, C., & Olatunde, T. I. (2024). Harnessing synergy between biomedical and electrical engineering: A comparative analysis of healthcare advancement in Nigeria and the USA. World Journal of Advanced Engineering Technology and Sciences, 11(2), 628–649.
[35]Sbarski, P., & Kroonenburg, S. (2020). Serverless architectures on AWS: With examples using AWS Lambda. Manning Publications.