Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 808-814· 0 citations· 14 references
Abstract
The rapid evolution of enterprise systems toward cloud-native environments has introduced significant improvements in scalability and flexibility, while also increasing architectural complexity. Traditional solution architectures struggle to handle dynamic workloads, heterogeneous infrastructures, and real-time decision-making requirements. This paper proposes an AI-driven enterprise solution architecture designed to enhance scalability, resilience, and intelligent orchestration in cloud-native systems. The framework integrates artificial intelligence across multiple layers, including resource provisioning, service orchestration, anomaly detection, and adaptive scaling. Unlike conventional rule-based approaches, the architecture leverages data-driven intelligence to optimize system performance and resource utilization while maintaining reliability. Key components include microservices-based design, container orchestration, event-driven communication, and AI-enabled control mechanisms. The architecture emphasizes modularity, interoperability, and continuous learning to ensure adaptability across diverse enterprise applications. Security and governance are incorporated following DevSecOps practices. The proposed solution effectively addresses operational inefficiencies, latency issues, and scalability bottlenecks, providing a robust foundation for next-generation intelligent enterprise systems.
The paper addresses the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally, and presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments.
Kanya Mohammed, Naree Thongchai· International Journal of Mod...· 0 citations
The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
Modern enterprises generate massive volumes of data from cloud platforms, IoT devices, enterprise applications, social media, and AI systems, creating challenges in data integration, governance, scalability, security, and real-time analytics. Traditional data management approaches often struggle to handle these complex and distributed environments. This paper proposes an Autonomous Data Fabric (ADF) architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems. The framework supports automated data discovery, semantic integration, adaptive workflows, continuous monitoring, and intelligent resource optimization while ensuring data quality, security, and compliance. Experimental results demonstrate that the proposed ADF significantly improves data integration efficiency, governance, analytics performance, operational cost, and decision-making compared to conventional systems. Its scalable and self-adaptive design supports hybrid cloud, multi-cloud, edge, and on-premises environments, making it a robust solution for enterprise digital transformation and next-generation intelligent data management.
Narendra Karmarkar· International Journal of Dat...· 0 citations
An AI-enabled enterprise platform engineering framework for scalable developer platforms, intelligent infrastructure automation, and operational excellence is developed that indicates that combining self-service workflows with governed AI assistance can improve process consistency, reduce operational handoffs, strengthen continuous compliance, and support earlier detection and resolution of infrastructure failures.
Bhanu Kiran Kumar Muggalla· International Journal of Int...· 0 citations
The rapid evolution of cloud computing has transformed enterprise software development by enabling scalable infrastructure, distributed applications, microservices architectures, and increasingly frequent software releases. However, the growing complexity of cloud-native environments introduces challenges related to deployment latency, pipeline failures, resource inefficiency, service availability, and recovery from unsuccessful releases. This study examines the optimization of cloud-native Continuous Integration and Continuous Deployment (CI/CD) workflows through DevOps automation, with emphasis on improving deployment speed, resilience, and operational efficiency across enterprise environments. The proposed approach integrates automated code integration, testing, containerization, infrastructure as code, Kubernetes orchestration, continuous monitoring, policy-driven deployment, and automated rollback mechanisms within an integrated delivery pipeline. Performance is evaluated using deployment frequency, lead time, pipeline execution time, change failure rate, mean time to recovery, resource utilization, and service availability. The study further investigates pipeline bottlenecks and the contribution of automation to deployment consistency, fault detection, scalability, and recovery performance. The resulting framework provides a systematic basis for balancing rapid software delivery with reliability, resilience, and efficient infrastructure utilization across increasingly complex cloud-native enterprise systems and applications.
Destiny Pwul· International Journal of Res...· 0 citations
This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems, and reveals that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings.
A. Reza· International Journal of Mac...· 0 citations
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