SmartSAN AI: An AI-Powered Framework for Zero-Downtime Storage Area Network Migrations

SmartSAN AI represents a transformative framework designed to address the inherent complexities of Storage Area Network migrations in modern enterprise environments. As organizations increasingly transition to hybrid and multi-vendor storage ecosystems, traditional migration methodologies frequently result in extended downtime windows and significant operational disruption. The framework delivers a comprehensive solution through intelligent automation across the entire migration lifecycle, incorporating advanced machine learning capabilities for discovery, compatibility validation, and orchestration functions. By implementing a domain-driven microservices architecture with specialized components for data collection, analysis, zoning, provisioning, and cutover management, SmartSAN AI enables near-zero downtime migrations while dramatically reducing planning overhead and configuration errors. The system integrates seamlessly with leading storage platforms and existing IT service management workflows, automatically generating vendor-specific commands from standardized migration policies. Financial modeling demonstrates compelling return on investment through reduced operational costs, accelerated project timelines, and improved migration success rates. Comprehensive field validation confirms substantial benefits including dramatically shortened cutover windows, precise compatibility validation, and enhanced audit capabilities that maintain compliance with regulatory frameworks while enabling truly non-disruptive storage transformations.

Authors

Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n324654
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
1.49
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article

SmartSAN AI: An AI-Powered Framework for Zero-Downtime Storage Area Network Migrations

Mahipal Reddy Yalla
1 citations
European Journal of Computer Science and Information Technology
IoT and Edge/Fog Computing
1.49
article

SmartSAN AI: An AI-Powered Framework for Zero-Downtime Storage Area Network Migrations

Mahipal Reddy Yalla
article en
1 citations

Abstract

SmartSAN AI represents a transformative framework designed to address the inherent complexities of Storage Area Network migrations in modern enterprise environments. As organizations increasingly transition to hybrid and multi-vendor storage ecosystems, traditional migration methodologies frequently result in extended downtime windows and significant operational disruption. The framework delivers a comprehensive solution through intelligent automation across the entire migration lifecycle, incorporating advanced machine learning capabilities for discovery, compatibility validation, and orchestration functions. By implementing a domain-driven microservices architecture with specialized components for data collection, analysis, zoning, provisioning, and cutover management, SmartSAN AI enables near-zero downtime migrations while dramatically reducing planning overhead and configuration errors. The system integrates seamlessly with leading storage platforms and existing IT service management workflows, automatically generating vendor-specific commands from standardized migration policies. Financial modeling demonstrates compelling return on investment through reduced operational costs, accelerated project timelines, and improved migration success rates. Comprehensive field validation confirms substantial benefits including dramatically shortened cutover windows, precise compatibility validation, and enhanced audit capabilities that maintain compliance with regulatory frameworks while enabling truly non-disruptive storage transformations.

European Journal of Computer Science and Information TechnologyVol. 13(32)
Openalex Percentile: Top 17%
IoT and Edge/Fog Computing
1.49
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SmartSAN AI: An AI-Powered Framework for Zero-Downtime Storage Area Network Migrations — Mahipal Reddy Yalla · European Journal of Computer Science and Information Technology (2025) | TGRS Research Map | TGRS