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Research Paper | Computer Science and Engineering | Volume 15 Issue 4, April 2026 | Pages: 1315 - 1319 | India
Intelligent Cloud Resource Orchestrator: An Automated System for Cloud Resource Management and Workload Optimization
Abstract: Cloud computing constitutes a foundational pillar of contemporary IT infrastructure, enabling on-demand access to scalable computing resources including storage, servers, and software services over the internet. Despite its widespread adoption, the efficient management of cloud resources remains a persistent challenge, primarily due to the dynamic and unpredictable nature of workloads and fluctuating user demands. Conventional cloud management systems depend heavily on manual monitoring and static configuration strategies, frequently resulting in server overload, suboptimal resource utilization, and elevated operational expenditure. This paper presents the Intelligent Cloud Resource Orchestrator, a web-based automated system designed to address these limitations through continuous real-time monitoring of server performance metrics including CPU utilization, memory consumption, and network activity. Upon detection of overload conditions, the system autonomously redistributes workloads to available servers using Docker container technology, ensuring seamless application migration with minimal service disruption. A Django-based web dashboard provides real-time visualization and analysis of system performance across all connected nodes. The platform additionally incorporates an auto-scaling mechanism that provisions new server instances in response to rising demand and decommissions idle instances during periods of reduced activity. The proposed system demonstrably improves performance, reduces service downtime, optimizes resource utilization, and minimizes operational costs, establishing it as a practical and scalable solution for modern cloud infrastructure management.
Keywords: Cloud Computing, Resource Orchestration, Auto-Scaling, Docker, Load Balancing, Django, Real-Time Monitoring, Workload Migration, Container Management
How to Cite?: Athil Thomas, Preethi Thomas, "Intelligent Cloud Resource Orchestrator: An Automated System for Cloud Resource Management and Workload Optimization", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 1315-1319, https://www.ijsr.net/getabstract.php?paperid=SR26421100828, DOI: https://dx.dx.doi.org/10.21275/SR26421100828