Optimizing Cloud-Native CI/CD Workflows Using DevOps Automation for Improved Deployment Speed, Resilience, and Operational Efficiency Across Enterprises.
Aug 2026· International Journal of Research Publication and Reviews· Vol 7, pp. 1753-1766· 0 citations
TL;DR
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 to provide a systematic basis for balancing rapid software delivery with reliability, resilience, and efficient infrastructure utilization across increasingly complex cloud-native enterprise systems and applications.
Abstract
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.
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
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
In this project, one will be working on building a Continuous Integration and Continuous Deployment pipeline using Microsoft Azure to automate software delivery and improve overall efficiency.
Ashwani Kumar, Geetanjali Amarawat· International Journal for Re...· 0 citations
Legacy enterprise systems continue to support critical business operations, but many of these systems are monolithic, tightly coupled, difficult to scale, and vulnerable to security risks. These limitations reduce their ability to adapt to modern digital environments that require flexibility, reliability, faster deployment, and continuous security. This paper presents a structured framework for cloud-native modernization of legacy enterprise systems by integrating Artificial Intelligence and DevSecOps practices. The proposed framework supports gradual transformation through microservice decomposition, containerization, API-based interoperability, and hybrid integration, allowing organizations to modernize existing systems without major disruption to business processes. Artificial Intelligence is used to support intelligent code analysis, dependency mapping, anomaly detection, workload optimization, and migration planning. These AI-driven capabilities help identify risks, reduce manual effort, and improve decision-making during modernization. DevSecOps practices are integrated into the software development lifecycle through automated CI/CD pipelines, vulnerability scanning, compliance validation, and continuous monitoring. This ensures that security is not treated as a final-stage activity but is continuously applied from development to deployment and post-migration operations. The framework also addresses important migration concerns such as data integrity, system resilience, interoperability, and governance in hybrid cloud environments. Data consistency is maintained through controlled synchronization, validation procedures, and API-led integration between legacy and modernized components. The proposed model improves operational efficiency, deployment agility, security readiness, and system reliability by combining cloud-native architecture with intelligent automation and continuous security enforcement. This study provides a systematic modernization approach that connects legacy enterprise systems with scalable, secure, and future-ready cloud-native architectures.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· International Conference on...· 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
Cloud-based applications require architectures that can maintain efficient application loading and performance as workload increases. This study developed and experimentally evaluated three functionally equivalent e-commerce implementations: Monolithic, Baseline Microservices, and Proposed Microservices architectures. An experimental research methodology was adopted, with the same business logic, database, and workload scenarios maintained across the implementations. The Proposed architecture integrated Docker, Kubernetes on Amazon Elastic Kubernetes Service (EKS), horizontal pod autoscaling, load balancing, Redis caching, and CloudFront delivery. Controlled workloads were generated using k6, and performance was assessed using startup time, deployment availability, response time, throughput, autoscaling, resource utilisation, inter-service latency, full user-journey performance, cache effectiveness, and frontend loading metrics. Results showed that the Monolithic architecture had the shortest startup time (1.33 s), while the Proposed architecture required 25.36 s. Under 2,000 virtual users, however, the Proposed architecture achieved an average response time of 33.47 ms and throughput of 1,915.78 req/s, compared with 1,951.56 ms and 670.13 req/s for the Monolithic architecture. The Proposed architecture also recorded 0.00% deployment failure, scaled from 2 to 15 replicas, and reduced cache latency by 30.12% (p = 0.0001). Its frontend FCP (First Contentful Paint), LCP (Largest Contentful Paint) and TTI (Time to Interactive) were each 617 ms. The findings demonstrate that integrated cloud-native mechanisms can substantially improve application performance and scalability under increasing workloads.
Ikediego Henrietta Onyinyechi, A. Godspower, Chekwube Georgina Nwankwo et al.· International journal of re...· 0 citations
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