Virtual Booth

Enterprise Linux systems continuously generate operational and authentication logs that must be retained for troubleshooting, auditing and security. Keeping all historical logs in high-access cloud storage can increase long-term cost, while manual verification of archive status reduces operational visibility. This project develops a Cost-Efficient Automated Cloud Log Management and Archiving System for Enterprise Virtual Infrastructure using Amazon Web Services (AWS). An Ubuntu 24.04 Amazon EC2 instance acts as the log source and application host. Python 3 and Boto3 collect selected Linux logs, compress them into timestamped .tar.gz archives and upload them to Amazon S3 Standard on an hourly Linux cron schedule. A 14-day S3 Lifecycle rule is designed to transition eligible objects to S3 Glacier Flexible Retrieval as the principal Cost Optimization mechanism. A Flask web dashboard provides centralized visibility of archive count, stored volume, recent uploads, storage class and estimated storage cost. Amazon SNS provides lifecycle-related email notification, while Amazon CloudWatch and collector.log provide supporting operational evidence. The project follows a cloud-oriented Agile-DevOps methodology and an AWS Well-Architected review process. Evaluation is designed around controlled 10-50 GB workloads, comparing an all-S3-Standard baseline with the lifecycle-managed model and validating dashboard accuracy and notification delivery. Because AWS Learner Lab sessions are time limited, natural lifecycle transition and immediate demonstration paths are reported separately to avoid claiming that a 14-day lifecycle transition occurs instantly. The project therefore presents a lightweight AWS-native design for automated log retention, centralized visibility, notification and measurable storage-cost optimization.Enterprise Linux systems continuously generate operational and authentication logs that must be retained for troubleshooting, auditing and security. Keeping all historical logs in high-access cloud storage can increase long-term cost, while manual verification of archive status reduces operational visibility. This project develops a Cost-Efficient Automated Cloud Log Management and Archiving System for Enterprise Virtual Infrastructure using Amazon Web Services (AWS). An Ubuntu 24.04 Amazon EC2 instance acts as the log source and application host. Python 3 and Boto3 collect selected Linux logs, compress them into timestamped .tar.gz archives and upload them to Amazon S3 Standard on an hourly Linux cron schedule. A 14-day S3 Lifecycle rule is designed to transition eligible objects to S3 Glacier Flexible Retrieval as the principal Cost Optimization mechanism. A Flask web dashboard provides centralized visibility of archive count, stored volume, recent uploads, storage class and estimated storage cost. Amazon SNS provides lifecycle-related email notification, while Amazon CloudWatch and collector.log provide supporting operational evidence. The project follows a cloud-oriented Agile-DevOps methodology and an AWS Well-Architected review process. Evaluation is designed around controlled 10-50 GB workloads, comparing an all-S3-Standard baseline with the lifecycle-managed model and validating dashboard accuracy and notification delivery. Because AWS Learner Lab sessions are time limited, natural lifecycle transition and immediate demonstration paths are reported separately to avoid claiming that a 14-day lifecycle transition occurs instantly. The project therefore presents a lightweight AWS-native design for automated log retention, centralized visibility, notification and measurable storage-cost optimization.