Advancing Cloud Infrastructure Supportability: A Framework for Digital Inclusion and Technical Empowerment
DOI:
https://doi.org/10.22399/ijcesen.5088Keywords:
Cloud Infrastructure Supportability, Guided Troubleshooting Systems, Self-Service Diagnostics, Knowledge Management Architecture, Digital Inclusion FrameworkAbstract
Cloud infrastructure supportability includes the use of knowledge management, diagnostic automation, and guided fault isolation processes at scale to make subject matter expertise consumable across many user segments. Knowledge stores help to achieve consistent regional diagnostics across distributed engineering teams using documented processes for troubleshooting and resolution of issues. Self-service diagnostic systems can provide insight into problems and bottlenecks from telemetry, configurations, and operational behavior. Guided troubleshooters implement diagnostic workflows, possibly spanning more than one domain and following an iterative process, gradually narrowing down the possibilities and helping the user understand how components of the infrastructure are related. Documentation is used for self-service by customers and to ensure a common understanding of the capabilities of support services. The telemetry from each support interaction is used in the iterative improvement of documentation and automated diagnostic logic. These feedback loops between internal knowledge, customer-facing diagnostics, and publicly available documentation increase the dependability of service, even lowering the technical knowledge barrier. This tri-layer supportability model accelerates the adoption of cloud across economies and geographies by making support models from reactive to proactive available in a systematic manner.
References
[1] Chin-Wei Huang et al., "Resilient and Reliable Cloud Network Control for Mission-Critical Latency-Sensitive Service Chains," arXiv, 26th Nov. 2025. Available: https://arxiv.org/html/2511.21960v1
[2] Pengfei Chen et al., "Making Availability as a Service in the Clouds," arXiv, Mar. 2025. Available: https://arxiv.org/pdf/1503.04422
[3] Gireesh Kambala, "Designing resilient enterprise applications in the cloud: Strategies and best practices," WJARR, 2023. Available: https://wjarr.com/sites/default/files/WJARR-2023-0303.pdf
[4] Mr. Bibhu Kalyan Mishra and Prof. (Dr.) S.K. Yadav, "High Availability Of Resource In Cloud Computing Technology: Review, Issues And Challenges," IJETT, 2020. Available: https://ijettjournal.org/assets/Volume-68/Issue-1/IJETT-V68I1P205.pdf
[5] Chukwunonso Henry Nwokoye et al., "A Survey Of Accessible Explainable Artificial Intelligence Research," arXiv, 2024. Available: https://arxiv.org/pdf/2407.17484
[6] Paula Bajdor, "Cloud Computing in Terms of Sustainable Development – Evaluation and Mutual Relations," ScienceDirect. 2023. Available: https://www.sciencedirect.com/science/article/pii/S1877050923011778
[7] Shengwei Chen et al., "Modeling Conceptual Characteristics of Virtual Machines for CPU Utilization Prediction," arXiv, 2018. Available: https://arxiv.org/pdf/1811.04731
[8] Anjali and Michael M. Swift, "Locked In, Leaked Out: Measuring Isolation via Kernel Locks," arXiv, Jul. 2025. Available: https://arxiv.org/html/2507.21248v1
[9] Zhenhao Zhou et al., "Benchmarking and Enhancing LLM Agents in Localizing Linux Kernel Bugs," arXiv, May 2025. Available: https://arxiv.org/pdf/2505.19489
[10] Benedict Schlüter et al., "WeSee: Using Malicious #VC Interrupts to Break AMD SEV-SNP," arXiv, 2024. Available: https://arxiv.org/html/2404.03526v1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
