VOLUME 15, ISSUE 3, MARCH 2026

Abstract: Modern software systems require flexible and scalable deployment environments to support growing workloads and complex applications. Containerization has emerged as an efficient solution that allows applications to be packaged with all required dependencies into lightweight containers. Docker is one of the most widely used containerization technologies that enables developers to build portable and consistent application environments. However, managing containers in large-scale environments requires a reliable orchestration platform. This research presents a cloud-based deployment architecture titled “Dockerized Microservices Architecture on AWS ECS.” The system integrates Docker containerization with several Amazon Web Services (AWS) components including Amazon Elastic Container Service (ECS), Amazon Elastic Container Registry (ECR), Amazon Elastic Compute Cloud (EC2), and AWS Identity and Access Management (IAM). Docker is used to build container images for the application, while Amazon ECR stores these images securely in private repositories. Amazon ECS is responsible for container orchestration, enabling automated deployment, monitoring, and scaling of containerized applications. Amazon EC2 provides the computing infrastructure required to run containers within the ECS cluster. IAM ensures secure authentication and role-based access control for cloud resources. The proposed system demonstrates a scalable and secure container deployment model that improves application portability, deployment efficiency, and resource utilization. The results show that combining Docker with AWS ECS can significantly simplify cloud deployment and support modern DevOps practices for building reliable microservices-based applications.

Keywords: Docker, Microservices Architecture, AWS ECS, Cloud Deployment, Containerization, DevOps.

Abstract: The rapid growth of digital communication has increased the need for efficient methods of sharing web links across platforms. Long and complex URLs often reduce readability, are difficult to manage, and are not suitable for sharing in environments such as social media, emails, or printed documents. This paper presents SkyLink, a cloud-native URL shortening platform designed to generate concise and trackable links while providing analytics on user engagement. SkyLink is developed using a modern full-stack architecture that combines React and TypeScript for the frontend and Supabase as a Backend-as-a-Service platform. The system enables users to generate shortened URLs, track click statistics, and manage links through an interactive dashboard. Containerization using Docker ensures portability, while Infrastructure as Code implemented through Terraform allows automated provisioning of cloud resources on AWS. The system architecture emphasizes scalability, reliability, and ease of deployment. By integrating modern DevOps practices such as containerization, automated CI/CD pipelines, and serverless infrastructure, SkyLink demonstrates how contemporary web technologies can be used to build scalable and production-ready applications. Experimental evaluation shows that the system performs efficiently in generating short URLs and handling user requests with minimal latency. The proposed platform provides a cost-effective and flexible alternative to existing commercial URL shortening services.

Abstract: Manual cloud infrastructure management leads to environment inconsistencies, deployment errors, and significant operational overhead. Existing productivity timer applications suffer from poor visual engagement and lack modern deployment practices. This paper presents Focus Timer, a React 19 web application with a glassmorphic interface, adaptive time-of-day theming, and SVG-based circular progress visualization, deployed to AWS ECS Fargate through a fully automated pipeline using Docker and AWS CloudFormation. The multi-stage Docker build produces a production image of approximately 25 MB. The Vite 7 build generates a gzipped bundle under 50 KB with sub-second page load. CloudFormation templates reproducibly provision an ECS Cluster, IAM Execution Role, and Fargate Task Definition across any AWS region. Automated PowerShell scripts complete the full pipeline from source to running cloud service in under six minutes, eliminating all manual console steps. All 20 functional test cases passed across Chrome, Firefox, Edge, and Safari.

Keywords: React 19, Vite 7, AWS CloudFormation, Docker, ECS Fargate, Infrastructure-as-Code, Glassmorphism, SVG Animations, ECR.

Abstract: Cloud computing and containerization technologies have fundamentally transformed modern software application deployment. Traditional deployment methods required manual server configuration, dependency management, and environment maintenance—processes that were time-consuming and error-prone. This project demonstrates the complete, end-to-end process of Dockerizing a Node.js application and deploying it on an Amazon Elastic Compute Cloud (EC2) instance. Docker encapsulates the application and its runtime environment into a portable container image, ensuring consistent behavior across environments. The project covers EC2 instance provisioning on Amazon Linux 2023, Docker Engine installation, container lifecycle management, AWS Security Group configuration, port mapping, and comprehensive deployment verification through terminal commands and browser-based testing. Results confirm successful deployment with the application accessible via public IPv4, validating the effectiveness of containerized cloud deployment as a modern DevOps solution.

Keywords: Docker, Node.js, Amazon Web Services (AWS), Amazon EC2, Containerization, Cloud Deployment, DevOps, Docker Image, Dockerfile, Security Groups, Port Mapping, Amazon Linux 2023, CI/CD.

Abstract: In the modern digital world, organizations generate large volumes of data every day. Managing and analyzing this data efficiently is a critical challenge for businesses. Traditional data processing systems rely on manual effort and server based infrastructure, making them slow, expensive, and difficult to scale. This project proposes a Serverless Data Aggregation and Automated Reporting System using Amazon Web Services (AWS). The system leverages AWS Lambda for serverless data processing, Amazon DynamoDB for scalable cloud based data storage, Amazon S3 for secure report storage, Amazon EventBridge for automated scheduling, and Amazon CloudWatch for real time monitoring. Sales transaction data stored in DynamoDB is automatically processed by Lambda functions that generate structured CSV reports uploaded to S3 at scheduled intervals. The proposed system eliminates manual reporting overhead, reduces infrastructure costs, and provides a scalable, reliable, and cost effective solution for real world data aggregation and business reporting requirements.

Keywords: AWS Lambda, Serverless Computing, Amazon DynamoDB, Amazon S3, Cloud Computing, Data Aggregation, Automated Reporting, Amazon EventBridge, Python.

Abstract: In modern cloud computing environments, automation and scalability are essential for application deployment. Traditional deployment methods involve manual server configuration, dependency installation, and repeated environment setup, which lead to configuration errors and increased deployment time. This paper presents a system titled 'AWS CloudFormation for Automated Docker Container Deployment,' which automates the process of deploying containerized applications using cloud infrastructure. A Python Flask web application is containerized using Docker and stored in Amazon Elastic Container Registry (ECR). Deployment is managed through Amazon Elastic Container Service (ECS), while AWS CloudFormation automates infrastructure provisioning following the Infrastructure as Code (IaC) paradigm. A CI/CD pipeline using AWS CodePipeline and AWS CodeBuild enables automatic build and deployment on code changes. An Application Load Balancer (ALB) ensures high availability by distributing user traffic across running containers. Experimental results demonstrate that the proposed system significantly reduces deployment time and eliminates manual configuration errors.

Keywords: AWS CloudFormation, Docker, Amazon ECS, Amazon ECR, CI/CD Pipeline, Infrastructure as Code, Container Deployment, Application Load Balancer, DevOps, Cloud Computing.

Abstract: Modern software systems demand deployment strategies that eliminate downtime and ensure continuous service availability. Traditional in-place deployments cause service interruptions ranging from minutes to hours, directly impacting user experience, customer trust, and business revenue. This project implements a Blue/Green Deployment strategy for a containerized Node.js e-commerce web application hosted on Amazon Web Services (AWS). The system leverages Docker for containerization, AWS Elastic Container Registry (ECR) for versioned image storage, AWS EC2 for compute infrastructure, and an AWS Application Load Balancer (ALB) for intelligent traffic routing between Blue and Green environments. The Blue environment serves live production traffic, while the Green environment hosts the updated application version. Following automated health-check validation, the ALB listener rule is updated to switch traffic from Blue to Green in under one second, achieving true zero-downtime deployment. Emergency rollback reverses the switch in under 30 seconds without service interruption. Evaluation results confirm that the system sustains over 450 requests per second with average response times below 115 ms, while all health check test cases pass successfully. The project provides a reproducible, enterprise-grade reference architecture for DevOps practitioners adopting continuous deployment pipelines.

Keywords: Blue/Green Deployment, Docker, AWS EC2, AWS ECR, Application Load Balancer, Zero-Downtime Deployment, Node.js, DevOps, Containerization, Cloud Computing, Continuous Delivery.

Abstract: Modern web applications require scalable and efficient backend systems to manage large volumes of requests and data. Traditional server-based architectures often require complex infrastructure management and maintenance. To address these limitations, serverless computing has emerged as an effective solution for building scalable and cost-efficient APIs. This paper presents the design and implementation of a Serverless API using AWS Lambda and DynamoDB. The system leverages Amazon Web Services to build a fully serverless architecture that eliminates the need for server management while providing automatic scaling and high availability. AWS Lambda is used to execute backend logic, while Amazon DynamoDB serves as a highly scalable NoSQL database for data storage. The proposed system enables developers to build lightweight, scalable APIs that can process requests efficiently without maintaining traditional server infrastructure. The evaluation results demonstrate that serverless architecture reduces operational overhead, improves scalability, and optimizes resource utilization.

Keywords: Serverless Computing, AWS Lambda, DynamoDB, Cloud Computing, REST API, Serverless Architecture

Abstract: With the increasing dependence on digital data, the need for reliable backup and data recovery mechanisms has become critical. Data loss may occur due to hardware failure, accidental deletion, cyber threats, or system crashes. Traditional backup systems often rely on manual copying or scheduled backups, which are time-consuming and prone to human errors. This research proposes an Automated Cloud Backup System using Amazon Web Services (AWS) that ensures secure and automatic replication of files in a cloud environment. The system utilizes Amazon S3 for scalable object storage, AWS Lambda for serverless automation, IAM for secure access management, and CloudWatch for monitoring system activity. Whenever a file is uploaded to the primary S3 bucket, an event notification automatically triggers the Lambda function. The Lambda function then replicates the file into a secondary backup bucket, ensuring that a duplicate copy of the data is always available. This event-driven architecture eliminates manual backup processes and improves data reliability. The experimental results demonstrate that the proposed system successfully performs automated file replication with minimal latency and high reliability. The system ensures efficient data protection and provides a scalable backup solution for modern cloud-based applications.

Keywords: Cloud Computing, Data Backup, Amazon Web Services, AWS Lambda, Amazon S3, Serverless Architecture

Abstract: Continuous Deployment (CD) is a modern DevOps practice that enables automatic building, testing, and deployment of applications whenever changes are pushed to the source code repository. This project focuses on implementing Continuous Deployment using GitLab CI/CD pipelines and Docker container technology to automate the software delivery process. The system integrates version control, automated pipeline execution, container image creation, testing, and deployment into a seamless workflow. When a developer pushes code to the main branch, the GitLab CI/CD pipeline is automatically triggered. The application is built into a Docker image, tested, stored in a container registry, and deployed to a target server without manual intervention. By containerizing the application using Docker, consistency across development, testing, and production environments is ensured. GitLab CI/CD manages the automation stages including build, test, and deploy, reducing human error and improving deployment speed and reliability. The implementation demonstrates how automated pipelines improve software quality, enhance team productivity, and support faster release cycles, providing a scalable foundation for future enhancements such as Kubernetes-based deployment and automated monitoring systems.

Keywords: Continuous Deployment, GitLab CI/CD, Docker, DevOps, Containerization, Automation, Pipeline, Software Delivery.

Abstract: This paper presents an AI-Driven Crime Reporting and Reward Management System to improve crime reporting efficiency and transparency. The system allows citizens to report crimes digitally with optional anonymity, while Machine Learning techniques are used for crime classification, fake report detection, and hotspot analysis. A reward mechanism encourages responsible reporting by evaluating the accuracy of submitted information. The system enhances decision-making, reduces response time, and strengthens trust between citizens and law enforcement.

Keywords: Artificial Intelligence, Crime Reporting, Machine Learning, Reward System, Crime Analysis

Abstract:  Phase diagrams are essential thermodynamic tools that describe equilibrium phase stability as functions of temperature and composition in alloy systems. They guide alloy design, heat-treatment optimization, and microstructural control in critical industries. However, experimental determination of phase diagrams is costly and time-intensive. Although computational approaches such as CALPHAD (Calculation of Phase Diagrams) provide reliable thermodynamic modelling through Gibbs free energy minimization, they depend on curated parameter databases and expert assessment, limiting rapid exploration of new material systems.

In this work, we introduce aLLoyM, a domain-adapted Large Language Model (LLM) developed for structured alloy phase diagram prediction. Thermodynamic data generated from CALPHAD assessments in the Computational Phase Diagram Database (CPDDB), covering 389 binary and 38 ternary systems, were systematically sampled to produce over 800,000 equilibrium data points. These data were transformed into multi-task Question–Answer (Q&A) pairs and used to finetune the Mistral-Nemo-Instruct model via Low-Rank Adaptation (LoRA), enabling efficient domain specialization.

The framework supports three thermodynamic reasoning tasks: full phase information prediction, phase name inference, and inverse experimental condition prediction. Performance was evaluated under both interpolation and extrapolation settings to assess generalization. Results show substantial improvement over baseline LLM performance and demonstrate the model’s ability to infer plausible phase behaviour for previously unseen systems.

These findings highlight the potential of integrating

Large Language Models with computational thermodynamics to develop scalable AI-assisted tools for accelerating alloy design and materials discovery.