Citation:

Mahi Ratan Reddy Deva, 2025. "AI-Driven Cloud Infrastructure: Advances in Kubernetes and Serverless Computing.", ESP Journal of Engineering & Technology Advancements  5(3): 126-134.

Abstract:

Artificial Intelligence (AI) has been integrated into cloud infrastructure, making it revolutionizing modern computing by automating, scaling, and improving efficiency. The first of these is Kubernetes, and the second is serverless computing. Kubernetes, a container orchestration platform, benefits from AI-driven enhancements in workload scheduling, auto-scaling, and resource optimization. By combining AI-based predictive analytics with container deployment, overhead is reduced in terms of operational overhead as well as fault tolerance. However, serverless computing takes away the management of infrastructure, leaving the developers free to write application logic. Serverless architectures with AI-based power can scale and adaptively allocate resources and execute cloud workloads with minimum cost. This review study examines the latest development of AI-based Kubernetes and serverless computing, their influence on the cloud infrastructure. AI is discussed insofar as its orchestration role can be optimized, security is improved, and self-healing cloud environments are made possible. The paper also studies challenges: latency, security issues, and uncertainties of the integration of AI models. Through the analysis of state-of-the-art innovation and future trends, this review covers how AI is impacting the future cloud native computing.

References:

[1] K. Anbalagan, “AI in Cloud Computing: Enhancing Services and Performance,” Int. J. Comput. Eng. Technol., vol. 15, pp. 622–635, 2024, doi: 10.5281/zenodo.13353681.

[2] K. Govindan et al., “Industry Surveys IT Consulting & Other Services.,” J. Clean. Prod., 2018.

[3] H. Chahed et al., “AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications,” Internet of Things, vol. 22, p. 100805, Jul. 2023, doi: 10.1016/j.iot.2023.100805.

[4] J. Decker, P. Kasprzak, and J. M. Kunkel, “Performance Evaluation of Open-Source Serverless Platforms for Kubernetes,” Algorithms, 2022, doi: 10.3390/a15070234.

[5] B. K. R. Janumpally, “A Review on Data Security and Privacy in Serverless Computing: Key Strategies, Emerging Challenges,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 3, p. 9, 2025.

[6] H. Martins, F. Araujo, and P. R. da Cunha, “Benchmarking Serverless Computing Platforms,” J. Grid Comput., 2020, doi: 10.1007/s10723-020-09523-1.

[7] A. Goyal, “Optimising Cloud-Based CI/CD Pipelines: Techniques for Rapid Software Deployment,” Tech. Int. J. Eng. Res., vol. 11, no. 11, pp. 896–904, 2024.

[8] J. Thomas, “The Effect and Challenges of the Internet of Things (IoT) on the Management of Supply Chains,” Int. J. Res. Anal. Rev., vol. 8, no. 3, pp. 874–878, 2021.

[9] T. Rausch, A. Rashed, and S. Dustdar, “Optimized container scheduling for data-intensive serverless edge computing,” Futur. Gener. Comput. Syst., 2021, doi: 10.1016/j.future.2020.07.017.

[10] V. Prajapati, “Cloud-Based Database Management: Architecture, Security, challenges and solutions,” J. Glob. Res. Electron. Commun., vol. 01, no. 1, pp. 07–13, 2025.

[11] S. R. P. Madugula and N. Malali, “Adversarial Robustness of AI-Driven Claims Management Systems,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 237–246, Mar. 2025, doi: 10.48175/IJARSCT-24430.

[12] S. Arora, S. R. Thota, and S. Gupta, “Artificial Intelligence-Driven Big Data Analytics for Business Intelligence in SaaS Products,” in 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT), IEEE, Aug. 2024, pp. 164–169. doi: 10.1109/IC2SDT62152.2024.10696409.

[13] S. U. Amin and M. S. Hossain, “Edge Intelligence and Internet of Things in Healthcare: A Survey,” IEEE Access, 2021, doi: 10.1109/ACCESS.2020.3045115.

[14] V. Gharibvand et al., “Cloud based manufacturing: A review of recent developments in architectures, technologies, infrastructures, platforms and associated challenges,” International Journal of Advanced Manufacturing Technology. 2024. doi: 10.1007/s00170-024-12989-y.

[15] R. P. Sola, N. Malali, and P. Madugula, Cloud Database Security: Integrating Deep Learning and Machine Learning for Threat Detection and Prevention. 2025.

[16] T. Subramanya and R. Riggio, “Centralized and Federated Learning for Predictive VNF Autoscaling in Multi-Domain 5G Networks and beyond,” IEEE Trans. Netw. Serv. Manag., 2021, doi: 10.1109/TNSM.2021.3050955.

[17] C. Perducat, D. C. Mazur, W. Mukai, S. N. Sandler, M. J. Anthony, and J. A. Mills, “Evolution and Trends of Cloud on Industrial OT Networks,” IEEE Open J. Ind. Appl., 2023, doi: 10.1109/OJIA.2023.3309669.

[18] V. Pillai, “Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets,” Int. J. Eng. Comput. Sci., vol. 12, no. 7, 2023.

[19] A. Gogineni, “Chaos Engineering in the Cloud-Native Era: Evaluating Distributed AI Model Resilience on Kubernetes,” J Artif Intell Mach Learn Data Sci 2024, vol. 3, no. 1, pp. 2182–2187, 2025.

[20] S. Padmakala, M. Al-Farouni, D. D. Rao, K. Saritha, and R. P. Puneeth, “Dynamic and Energy-Efficient Resource Allocation using Bat Optimization in 5G Cloud Radio Access Networks,” in 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON), IEEE, Aug. 2024, pp. 1–4. doi: 10.1109/NMITCON62075.2024.10699133.

[21] C. Carrión, “Kubernetes as a Standard Container Orchestrator - A Bibliometric Analysis,” J. Grid Comput., 2022, doi: 10.1007/s10723-022-09629-8.

[22] A. Gogineni, “Multi-Cloud Deployment with Kubernetes: Challenges, Strategies, and Performance Optimization,” Int. Sci. J. Eng. Manag., vol. 1, no. 02, 2022.

[23] J. Thomas, “Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics,” J. Emerg. Technol. Innov. Res., vol. 8, no. 9, pp. 357–364, 2021.

[24] H. Shafiei, A. Khonsari, and P. Mousavi, “Serverless Computing: A Survey of Opportunities, Challenges, and Applications,” ACM Comput. Surv., 2022, doi: 10.1145/3510611.

[25] S. S. S. Neeli, “Cloud Migration DBA Strategies for Mission-Critical Business Applications,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 11, pp. 591–598, 2023.

[26] V. Prajapati, “Role of Identity and Access Management in Zero Trust Architecture for Cloud Security : Challenges and Solutions,” pp. 6–18, 2025, doi: 10.48175/IJARSCT-23902.

[27] V. Pillai, “Anomaly Detection in Financial and Insurance Data-Systems,” J. AI-Assisted Sci. Discov., vol. 4, no. 2, 2024.

[28] J. M. O. Candel, A. Elouali, F. J. M. Gimeno, and H. Mora, “Cloud vs Serverless Computing: A Security Point of View,” in Lecture Notes in Networks and Systems, 2023. doi: 10.1007/978-3-031-21333-5_109.

[29] D. D. Rao, S. Madasu, S. R. Gunturu, C. D’britto, and J. Lopes, “Cybersecurity Threat Detection Using Machine Learning in Cloud-Based Environments: A Comprehensive Study,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 1, 2024.

[30] D. Loconte, S. Ieva, A. Pinto, G. Loseto, F. Scioscia, and M. Ruta, “Expanding the cloud-to-edge continuum to the IoT in serverless federated learning,” Futur. Gener. Comput. Syst., 2024, doi: 10.1016/j.future.2024.02.024.

[31] H. B. Hassan, S. A. Barakat, and Q. I. Sarhan, “Survey on serverless computing,” J. Cloud Comput., vol. 10, no. 1, p. 39, Jul. 2021, doi: 10.1186/s13677-021-00253-7.

[32] S. K. Mondal, R. Pan, H. M. D. Kabir, T. Tian, and H. N. Dai, “Kubernetes in IT administration and serverless computing: An empirical study and research challenges,” J. Supercomput., 2022, doi: 10.1007/s11227-021-03982-3.

[33] S. Shah and M. Shah, “Deep Reinforcement Learning for Scalable Task Scheduling in Serverless Computing,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 3, no. 12, pp. 1845–1852, Jan. 2025, doi: 10.56726/IRJMETS17782.

[34] M. Aazam, S. Zeadally, and K. A. Harras, “Fog Computing Architecture, Evaluation, and Future Research Directions,” IEEE Commun. Mag., vol. 56, no. 5, pp. 46–52, May 2018, doi: 10.1109/MCOM.2018.1700707.

[35] S. Murri, “Data Security Environments Challenges and Solutions in Big Data,” Int. J. Curr. Eng. Technol., vol. 12, no. 6, pp. 565–574, 2022.

[36] J. K. Chaudhary, S. Tyagi, H. P. Sharma, S. V. Akram, D. R. Sisodia, and D. Kapila, “Machine Learning Model-Based Financial Market Sentiment Prediction and Application,” in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, May 2023, pp. 1456–1459. doi: 10.1109/ICACITE57410.2023.10183344.

[37] S. A. Ionescu and V. Diaconita, “Transforming Financial Decision-Making: The Interplay of AI, Cloud Computing and Advanced Data Management Technologies,” Int. J. Comput. Commun. Control, 2023, doi: 10.15837/ijccc.2023.6.5735.

[38] S. Arora and S. R. Thota, “Ethical Considerations and Privacy in AI-Driven Big Data Analytics,” Int. Res. J. Eng. Technol., vol. 11, no. 05, 2024.

[39] R. Ma, Y. Zhan, C. Wu, Z. Hong, Y. Ali, and Y. Xia, “Qora: Neural-Enhanced Interference-Aware Resource Provisioning for Serverless Computing,” IEEE Trans. Autom. Sci. Eng., pp. 1–16, 2025, doi: 10.1109/TASE.2025.3526197.

[40] H. T. Ciptaningtyas, R. R. Hariadi, F. D. Rosyadi, and S. S. Al Azmi, “Serverless Computing Model Using Kubernetes and Knative in a Scalable Cloud Development,” in 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C), 2024, pp. 659–664. doi: 10.1109/BTS-I2C63534.2024.10942132.

[41] M. M. Rahman, B. P. Pokharel, S. A. Sayeed, S. K. Bhowmik, N. Kshetri, and N. Eashrak, “riskAIchain: AI-Driven IT Infrastructure—Blockchain-Backed Approach for Enhanced Risk Management,” Risks, vol. 12, no. 12, p. 206, Dec. 2024, doi: 10.3390/risks12120206.

[42] C. K. Dehury and S. N. Srirama, “Integrating Serverless and DRL for Infrastructure Management in Streaming Data Processing across Edge-Cloud Continuum,” in 2024 IEEE 44th International Conference on Distributed Computing Systems Workshops (ICDCSW), 2024, pp. 93–101. doi: 10.1109/ICDCSW63686.2024.00020.

[43] M. Pranata, A. Wijayanto, and M. F. Sidiq, “Serverless Autoscaling Metrics for Optimum Performance on Edge Computing,” in 2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, Dec. 2023, pp. 65–69. doi: 10.1109/ISRITI60336.2023.10467288.

[44] S. Tuli et al., “AI augmented Edge and Fog computing: Trends and challenges,” Journal of Network and Computer Applications. 2023. doi: 10.1016/j.jnca.2023.103648.

Keywords:

AI-Driven, Cloud Infrastructure, Kubernetes, Serverless Computing, Cloud-Native, Orchestration.