SUSTAINABLE GITOPS FRAMEWORK FOR AUTOMATED DEPLOYMENT AND ENERGY OPTIMIZATION IN CLOUD SYSTEMS

Abstract

Cloud computing has transformed enterprise information technology by enabling elastic infrastructure, containerized applications, microservice architectures, continuous delivery, and globally distributed digital services. However, the rapid growth of cloud workloads has increased energy consumption, infrastructure utilization, operational complexity, and environmental impact. Conventional deployment automation primarily optimizes release speed, reliability, scalability, and service availability while energy efficiency and carbon awareness are frequently treated as secondary concerns. This paper proposes a Sustainable GitOps Framework for automated deployment and energy optimization in cloud systems. The framework integrates declarative infrastructure management, Git-based desired-state control, automated reconciliation, container orchestration, workload telemetry, energyawareness indicators, carbon-intensity context, resource-right-sizing analytics, policy-driven deployment validation, sustainable scheduling, and continuous optimization. The proposed methodology treats sustainability as an operational property embedded within the deployment lifecycle rather than an external reporting activity. Application manifests, infrastructure configurations, resource limits, scaling policies, placement constraints, and sustainability objectives are maintained through version-controlled repositories and evaluated before deployment. Runtime observability continuously analyzes processor utilization, memory demand, workload intensity, idle capacity, deployment frequency, scaling behavior, node efficiency, and service-level performance. Energy-aware policies identify overprovisioned workloads, inefficient replicas, unnecessary idle resources, and deployment opportunities across lower-impact execution windows or infrastructure locations where operational constraints permit. Machine-learning analytics are incorporated to forecast workload demand and support proactive resource allocation without compromising reliability. Representative evaluation indicates that the proposed framework can reduce unnecessary compute allocation, improve average resource utilization, lower estimated energy consumption, decrease configuration drift, accelerate deployment recovery, and maintain acceptable service performance compared with conventional automation. The framework provides a practical foundation for green cloud computing, sustainable DevOps, energy-aware Kubernetes operations, automated infrastructure governance, and environmentally responsible enterprise computing.