Modernizing Legacy Systems with AI Orchestration: From Monoliths to Autonomous Micro services
DOI:
https://doi.org/10.15662/IJARCST.2022.0506013Keywords:
AI orchestration, legacy modernization, microservices, autonomous systems, machine learning, containerization, self-healing architecture, cloud-native transformation, DevOps, predictive analyticsAbstract
Modern enterprises continue to rely on legacy monolithic systems that restrict scalability, agility, and innovation. With the emergence of artificial intelligence (AI) and cloud-native technologies, organizations can transform these rigid systems into adaptive, autonomous ecosystems. This paper presents a comprehensive framework, for modernizing legacy applications using AI-driven orchestration, enabling a shift from monoliths to autonomous microservices. The proposed architecture leverages machine learning (ML)-based orchestration engines, predictive workload management, and self-healing pipelines to optimize performance and resilience. Through a combination of empirical analysis and simulation-based evaluation, the study demonstrates that AI orchestration can reduce deployment time by up to 60%, enhance fault tolerance by 45%, and minimize operational overheads. The paper also explores governance and security implications, offering a roadmap for organizations seeking to achieve intelligent, autonomous modernization at scale
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