Integrating Artificial Intelligence to Automate Performance and Chaos Engineering in Cloud-Native Architectures
DOI:
https://doi.org/10.32628/CSEIT251112356Keywords:
Artificial Intelligence, Chaos Engineering, Cloud-Native Architecture, Performance Optimization, Machine Learning, Automated Testing, System Resilience, Fault InjectionAbstract
Cloud-native architectures have revolutionized modern software systems, yet they present unique challenges in maintaining performance and reliability at scale. This article presents a comprehensive framework for integrating artificial intelligence into performance and chaos engineering processes, addressing the limitations of traditional manual testing approaches. This article introduces novel techniques for automating fault injection, performance optimization, and system resilience through machine learning and deep learning models. This article demonstrates how AI-driven automation can enhance the detection of performance bottlenecks, predict potential system failures, and facilitate real-time remediation in cloud-native environments. Through extensive case studies across e-commerce, financial services, and media streaming sectors, this article validates the effectiveness of its approach in improving system reliability and operational efficiency. The framework provides significant advantages over conventional methods by reducing human intervention, accelerating issue detection, and enabling proactive system optimization. Furthermore, it establishes a practical roadmap for organizations to implement AI-driven performance and chaos engineering, contributing to the evolving landscape of cloud-native architecture management. Future research directions and potential improvements are discussed, highlighting the transformative potential of AI automation in building resilient cloud-native systems.
Downloads
References
Pini Reznik, "Cloud Native Transformation -Practical Patterns for Innovation," Container Solutions, 2024. [Online]. Available: https://www.container-solutions.com/hubfs/Arien%20Sketches/Cloud%20native%20survival%20kit%20-%20OReilly%20Software%20Architecture%20Conference%20in%20Berlin/Cloud%20Native%20Transformation%20-%20OReilly%20SW%20Arch.pdf
NetSpyGlass, "Performance monitoring automation puts network operations on the fast path to reliability engineering," NetSpyGlass Technical Documentation, 2019. [Online]. Available: https://www.netspyglass.com/wp-content/uploads/2021/04/nsgbrochure.pdf
Nilayam Kumar Kamila et al., "Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach," ScienceDirect, vol. 34, no. 10, Nov. 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1319157822003524
Akhter Banu, "Cloud-Native Real-Time Data Analytics Powered by Generative AI on Google Cloud Platform," ResearchGate, Nov. 2024. [Online]. Available: https://www.researchgate.net/publication/385619051_Cloud-Native_Real-Time_Data_Analytics_Powered_by_Generative_AI_on_Google_Cloud_Platform
Juan Hernández-Serrato et al., "Applying Machine Learning with Chaos Engineering," IEEE Xplore, 4 Jan. 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9307734
Hugo Sequeira et al., "Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites," IEEE Xplore, 2 Feb. 2015. [Online]. Available: https://ieeexplore.ieee.org/document/7027546
Prathyusha Nama, "Integrating AI with cloud computing: A framework for scalable and intelligent data processing in distributed environments," International Journal of Scientific Research and Applications, vol. 6, no. 2, 28 June 2022. [Online]. Available: https://ijsra.net/sites/default/files/IJSRA-2022-0119.pdf
Thejaswi Adimulam, "Scalable Architectures For Generative AI In Advanced Cloud Computing Environments: Enhancing Performance And Efficiency," International Journal of Creative Research Thoughts, vol. 10, no. 9, 9 Sep. 2022. [Online]. Available: https://ijcrt.org/papers/IJCRT2209539.pdf
Shiza Malik, et al., "Artificial intelligence and industrial applications-A revolution in modern industries," Ain Shams Engineering Journal, Vol. 15, no. 9, Sep. 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2090447924002612
Nicola Tamascelli et al., "Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning," Journal of Loss Prevention in the Process Industries, vol. 90, Aug. 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950423024001013
Olayiwola Blessing Akinnagbe, "The Future of Artificial Intelligence: Trends and Predictions," ResearchGate, Nov. 2024. [Online]. Available: https://www.researchgate.net/publication/385890167_The_Future_of_Artificial_Intelligence_Trends_and_Predictions
Abhishek Gupta, et al., "Cloud-Native ML: Architecting AI Solutions for Cloud-First Infrastructures," ResearchGate, Dec. 2024. [Online]. Available: https://www.researchgate.net/publication/387222365_Cloud-Native_ML_Architecting_AI_Solutions_for_Cloud-First_Infrastructures
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.