Graph-Based Network Modeling for Hybrid Infrastructure: Representing and Automating Dependencies Across On-Prem and Cloud Resources

Sai Raghu Ram Gummadidala

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 1069-1078

Abstract: Infrastructure designs using on-premises resources and cloud computing services have become vital to contemporary enterprise systems because of their scalability, flexibility, and resource optimization advantages. Nevertheless, dependency analysis and monitoring in hybrid cloud and on-premises infrastructures remain challenging tasks because of the complexity inherent in such designs. Traditional infrastructure management systems lack functionality for automatic dependency discovery and management, efficient automation, and monitoring scalability. This study proposes an innovative approach based on Graph-Based Network Modeling for representation and automation of dependencies in hybrid cloud and on-premises infrastructure systems. In this model, the infrastructure resources are represented by graph nodes while the dependencies by graph edges. Such design allows for effective visualization, dependency tracking, fault analysis, and orchestration management in hybrid cloud and on-premises infrastructure systems. Moreover, the model offers efficient infrastructure monitoring, automatic recovery operation execution, and dependency discovery. Experimental evaluation shows that the designed graph model improves performance in terms of increased accuracy in dependency detection, decreased fault recovery time, effective communication and resource optimization, as well as increased infrastructure reliability.

Keywords: Hybrid Infrastructure; Graph-Based Modeling; Dependency Automation; Cloud Computing; On-Premises Systems; Infrastructure Orchestration; Network Modeling; Distributed Systems; Dependency Analysis; Cloud-Native Infrastructure (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:1611

DOI: 10.32628/IJSRST251262084

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