AI Integration for Sustainable DevOps & Cloud Interdisciplinary: Framework Optimized Resource Management and Carbon Efficiency
Harish Chamarthi, Mohanraju Muppala
Abstract
The exponential growth of cloud computing and DevOps practices has significantly increased the carbon footprint of digital infrastructure, necessitating innovative approaches to sustainable software delivery. This paper proposes an artificial intelligence (AI)-driven framework that integrates interdisciplinary approaches from software engineering, operational efficiency, and environmental science to enhance sustainability in DevOps pipelines. Our methodology employs reinforcement learning for dynamic resource optimization, predictive analytics for carbon-aware workload scheduling, and an intelligent query fragment caching algorithm to minimize computational overhead. The framework leverages multi-objective optimization to balance performance requirements with environmental impact, incorporating real-time telemetry data from cloud resources. Experimental results demonstrate a potential 30-45% reduction in energy consumption and a 25-40% decrease in carbon emissions compared to conventional DevOps practices while maintaining service level objectives. The proposed architecture provides a scalable foundation for achieving greener software development lifecycle management without compromising operational efficiency.