Intelligent Cloud-Native DevOps Automation Framework: Deep Learning and AI-Guided Hybrid Fuzzy Model Combining WPM, TOPSIS, and PSO for Serverless Software Development
DOI:
https://doi.org/10.15662/IJRAI.2021.0406011Keywords:
Cloud-Native DevOps, Serverless Software Development, Deep Learning, AI-Guided Hybrid Fuzzy Model, Weighted Product Method (WPM), TOPSIS, Particle Swarm Optimization (PSO), Intelligent Automation, Multi-Criteria Decision-Making, Software Optimization, Cloud-Native ArchitectureAbstract
The shift towards cloud-native and serverless architectures has intensified the need for intelligent automation frameworks in software development and DevOps pipelines. This research presents an Intelligent Cloud-Native DevOps Automation Framework that integrates Deep Learning with AI-guided hybrid fuzzy models, leveraging Weighted Product Method (WPM), TOPSIS, and Particle Swarm Optimization (PSO) to optimize software development processes.
The framework addresses challenges in resource allocation, performance optimization, and decision-making under uncertainty. The hybrid fuzzy model captures the inherent vagueness in multi-criteria evaluation, while WPM and TOPSIS systematically rank alternative development strategies. PSO enhances optimization by dynamically adjusting parameters to maximize efficiency and minimize deployment latency.
By incorporating serverless computing, the framework enables scalable, event-driven, and cost-effective execution of DevOps pipelines. Experimental evaluation demonstrates significant improvements in automation efficiency, build reliability, and deployment speed, validating the framework’s capability to support self-adaptive, AI-powered software engineering in cloud-native environments. This study contributes a unified approach combining AI, fuzzy reasoning, optimization algorithms, and serverless cloud infrastructure for next-generation DevOps automation.
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