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Global Multidisciplinary Journal

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The Convergence of Hyperautomation and Autonomous Remediation: Mitigating Site Reliability Engineering Toil in Cloud-Native Ecosystems

DOI https://doi.org/10.5281/zenodo.19491192
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Jessica Killinpi 1 ,
4 Department of Computer Science and Engineering, Stanford University, United States of America

Abstract

The rapid evolution of cloud-native architectures has necessitated a fundamental shift in operational paradigms, moving away from manual intervention toward a sophisticated state of autonomous self-healing. This research explores the intersection of Site Reliability Engineering (SRE), hyperautomation, and Artificial Intelligence (AI) to address the persistent challenge of "toil"-repetitive, manual tasks that hinder scalability and contribute to professional burnout. By synthesizing current advancements in AI-driven fault prediction, automated incident response, and predictive analytics for multi-cloud environments, this paper proposes a holistic framework for safe autonomous remediation. The study investigates how modern organizations can transition from traditional DevOps practices to advanced SRE strategies that leverage machine learning for root cause analysis and proactive system stabilization. Special attention is given to the psychological and organizational impacts of toil, the technical requirements for self-healing Enterprise Resource Planning (ERP) systems, and the role of CI/CD automation in financial and technical data validation. The findings suggest that while the transition to fully autonomous systems presents significant technical and security challenges, the integration of predictive engines and automated remediation protocols is essential for the long-term reliability and sustainability of complex, high-scale software environments.

Keywords

Site Reliability Engineering, Autonomous Remediation, Toil Reduction, Hyperautomation

References

📄 Aazam, M., Zeadally, S., & Harras, K. A. Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems, 2018.
📄 Alkhanak, E. N., Itten, M. Z. A., & Aznam, N. K. A hyper-heuristic cost optimisation approach for scientific workflow scheduling in cloud computing. Future Generation Computer Systems, 2018.
📄 Ansarilari, Z., et al. A novel model for transfer synchronization in transit networks and a Lagrangian-based heuristic solution method. European Journal of Operational Research, 2024.
📄 Aramide, O. O. AI-driven automated incident response and remediation in networks. International Journal of Technology Management and Humanities, vol. 11, no. 02, pp. 1-9, 2025.
📄 Baghban, A., et al. Data-driven robust optimization for pipeline scheduling under flow rate uncertainty. Computers and Chemical Engineering, 2025.
📄 Bamoumen, M., et al. An efficient GRASP-like algorithm for the multi-product straight pipeline scheduling problem. Computers and Operations Research, 2023.
📄 Belacel, N., et al. A hybrid artificial fish swarm simulated annealing optimization algorithm for automatic identification of clusters, 2016.
📄 Bhat, S., Sirikonda, S. R., Katoch, V., and Jain, R. Carbon-Kube: A Kubernetes-Native Framework for Multi-Objective Carbon-Aware Scheduling of Big Data Pipelines. 2026 9th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), Kolkata, India, 2026, pp. 1-6. doi: 1109/IEMENTech202669403.2026.11434192.
📄 Cafaro, D. C., et al. Dynamic scheduling of multiproduct pipelines with multiple delivery due dates. Computers and Chemical Engineering, 2008.
📄 Castro, P. M., et al. Batch-centric scheduling formulation for treelike pipeline systems with forbidden product sequences. Computers and Chemical Engineering, 2019.
📄 Castro, P. M., et al. Product-centric continuous-time formulation for pipeline scheduling. Computers and Chemical Engineering, 2017.
📄 Cecil, A. E. Mismatch and burnout: An exploration of burnout and work passion amongst academic affairs professionals through an organizational lens. West Virginia University, 2021.
📄 Chen, H., et al. An MILP formulation for optimizing detailed schedules of a multiproduct pipeline network. Transp. Res. Part E Logist. Transp. Rev., 2019.
📄 Chen, J., et al. Network-based optimization modeling of manhole setting for pipeline transportation. Transp. Res. Part E Logist. Transp. Rev., 2018.
📄 Chou, et al. DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization. IEEE Syst J, 2018.
📄 Dalal, A. Exploring next-generation cybersecurity tools for advanced threat detection and incident response. Available at SSRN, p. 5424096, 2020.
📄 Durgam, S. CI/CD automation for financial data validation and deployment pipelines. Journal of Innovation and Sustainable Energy Management, 2025.
📄 Ganeeb, K., Tabbassum, A., Kethireddy, R. R., and Jabbireddy, S. AI-driven predictive analytics for multi-cloud management. In Intelligent Systems, pp. 225-238, CRC Press, 2024.
📄 George, S., George, A. H., Baskar, T., and Sujatha, V. The rise of hyperautomation: a new frontier for business process automation. Partners Universal International Research Journal, vol. 2, no. 4, pp. 13-35, 2023.
📄 Gupta, U., and Mahesh, V. A strategic roadmap for implementing site reliability engineering practices. Infosys Knowledge Institute, 2025.
📄 Han, P., et al. A double inference engine belief rule base for oil pipeline leakage. Expert Systems with Applications, 2024.
📄 iSmile Technologies. Top Site Reliability Engineering (SRE) Trends in 2023, 2023.
📄 Jangam, S. K., and Karri, N. Potential of AI and ML to Enhance Error Detection, Prediction, and Automated Remediation in Batch Processing. International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 4, pp. 70-81, 2022.
📄 Li, Z., et al. Two-stage optimization model for scheduling multiproduct pipeline network with multi-source and multi-terminal. Energy, 2024.
📄 Li, Z., et al. Scheduling of a branched multiproduct pipeline system with robust inventory management. Computers and Industrial Engineering, 2021.
📄 Mahmud, R., et al. Profit-aware application placement for integrated fog–cloud computing environments. J. Parallel Distrib. Comput., 2020.
📄 Mishra, S. K., et al. Load balancing in cloud computing: a big picture. J King Saud Univ Comput Inf Sci, 2018.
📄 Mohammed, M. R. Enhancing the Reliability of Cloud-Based Software Systems Using AI-Driven Fault Prediction and Auto-Remediation Techniques. American International Journal of Computer Science and Technology, vol. 3, no. 5, pp. 1-13, 2021.
📄 Muntala, P. S. R. P. The Future of Self-Healing ERP Systems: AI-Driven Root Cause Analysis and Remediation. International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 2, pp. 102-116, 2024.
📄 Murthy, S. Site Reliability Engineering and DevOps: Similarities and Differences. WaferWire.
📄 Nagaraj, V. Automating test vector validation for silicon verification at scale. International Journal of Engineering and Applied Sciences, 2025.
📄 Narayan, P. Intergenerational wealth transfer: Opportunities and challenges. World Research of Business Administration Journal, vol. 5, no. 3, Nov. 2025. doi: 10.56830/WRBA11202510.
📄 Sirikonda, S. R. Reducing SRE Toil via Safe Autonomous Remediation in Cloud-Native Systems. American Journal of Technology, 5(3), 30–49, 2026. doi: 10.58425/ajt.v5i3.511.
📄 Stergiou, C., et al. Secure integration of IoT and cloud computing. Futur Gener Comput Syst, 2018.
📄 Tortonesi, M., et al. Taming the IoT data deluge: An innovative information-centric service model for fog computing applications. Future Generation Computer Systems, 2019.
📄 Tsai, C.-W. SEIRA: AN effective algorithm for IoT resource allocation problem. Comput. Commun., 2018.
📄 Vasikarla, R. The Critical Role of Automation in Modern Site Reliability Engineering. IJSAT-International Journal on Science and Technology, vol. 16, no. 1, 2025.
📄 Wang, S., et al. Energy Minimization for Cloud Services with Stochastic Requests. Energy Minimization for Cloud Services, 2020.
📄 Xiang, B., et al. Intermolecular vibrational energy transfer enabled by microcavity strong light-matter coupling. Science, 2020.
📄 Zhou, X., et al. Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT. Future Generation Computer Systems, 2019.
📄 Zuo, L., et al. On self-adaptive threshold in cloud computing. Mob Netw Appl, 2016.

How to Cite

Jessica Killinpi. (2026). The Convergence of Hyperautomation and Autonomous Remediation: Mitigating Site Reliability Engineering Toil in Cloud-Native Ecosystems. Global Multidisciplinary Journal, 5(04), 13-20. https://doi.org/10.5281/zenodo.19491192

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