An Automated System for Deploying and Monitoring Cloud Instances Across Cloud Platform

Abstract

Cloud resource provisioning and monitoring are critical activities for organizations using cloud-based infrastructures. Manual management often leads to configuration errors, higher deployment time, and limited tracking. This work presents an automated system designed to deploy and monitor instances on a single cloud platform. The system uses a command-line interface (CLI) and integrated backend services to automate provisioning, configuration, performance monitoring, and fault reporting. Results demonstrate reduced deployment time, improved resource visibility, and lower manual dependency. Results demonstrate ~80% reduction in deployment time and higher reliability through automated monitoring

Introduction

The project introduces an automated system for deploying and monitoring cloud virtual machines, addressing the limitations of manual instance management, which is slow, error-prone, and difficult to scale. The system provides a unified command-line interface (CLI) and dashboard that allow users to launch, configure, and observe cloud instances in real time. Its architecture consists of four main components: a CLI for user commands, a backend controller that communicates with cloud APIs, cloud platform services for provisioning VMs, and a monitoring engine that collects performance metrics.

Users can deploy instances by specifying parameters such as image type, size, and region, while the monitoring module periodically gathers CPU and memory usage, running status, and error logs. The system also includes alerting and logging mechanisms to notify users when resource thresholds are exceeded. Implementation involves a Python-based CLI, a REST middleware backend, a single cloud provider for VM provisioning, and cloud monitoring APIs for metric extraction.

Conclusion

This system allows users to deploy and monitor cloud instances without manual control. With automation, efficiency improved and operational burden reduced. The system can be enhanced by adding multi-cloud support and predictive resource analytics. Future work includes multi-cloud integration and predictive analytics.

References

[1] S. Chinamanagonda, “Automating Infrastructure with Infrastructure as Code (IaaC),” SSRN Tech. Rep., 2019. SSRN [2] P. Ravindran, “Automating Multi-Cloud Infrastructure: Leveraging Terraform and IaaC for Scalable, Secure, and Efficient Cloud Management,” Int. J. Sci. Res. Compute. Sci. Eng. Inf. Technol., vol. 11, no. 2, pp. 2240-2247, Mar. 2025, doi: 10.32628/ [3] P. Mell and T. Grance, “The NIST definition of cloud computing,” NIST SP 800-145, 2011. [4] Amazon Web Services, “Amazon EC2 Documentation.” [5] Google Cloud Platform, “Compute Engine Documentation.”

Copyright

Copyright © 2025 Prof. Kalyani Sonawane, Pawar Rohan, Sathe Vedant, Waje Mohan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.