Advancing Cloud Infrastructure Supportability: A Framework for Digital Inclusion and Technical Empowerment - Sampath Rao Madarapu (International Journal of Computational and Experimental Science and Engineering, 2026)
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.