Microservice Modernization for Enterprise Service Migration Using AWS Cloud Architecture (Published)
Enterprises operating large, monolithic service-oriented platforms increasingly face constraints in scalability, deployment velocity, and operational resilience. This paper presents a structured approach to modernizing enterprise monolithic applications into cloud-native microservices deployed on Amazon Web Services (AWS). We propose a six-phase migration methodology grounded in domain-driven design and the strangler fig pattern, enabling incremental extraction of business capabilities without disrupting production systems. A reference AWS architecture is presented, combining Amazon ECS and EKS for container orchestration, Amazon API Gateway for edge routing, AWS App Mesh for service-to-service communication, and a polyglot persistence layer spanning Amazon Aurora, DynamoDB, and ElastiCache. The proposed methodology was evaluated through a case-style implementation involving decomposition of a legacy order-management monolith into independently deployable services. Empirical results indicate a 68% reduction in average response time, a twelvefold increase in deployment frequency, and substantially improved mean time to recovery compared to the legacy baseline. The paper also examines cost implications, security controls, and organizational readiness factors that influence migration outcomes, concluding with recommendations for enterprises planning similar modernization initiatives.
Keywords: AWS, Cloud-Native Architecture, DevOps, Legacy modernization, container orchestration, domain-driven design, enterprise migration, microservices, service mesh, strangler fig pattern
Ethical and Privacy Implications of Cloud AI in Financial Services (Published)
The financial services sector has increasingly integrated cloud computing architectures and Artificial Intelligence (AI) technologies to enhance customer engagement, streamline operational processes, and maintain a competitive edge. While these advancements bring substantial benefits, they also introduce complex ethical considerations and privacy vulnerabilities. This paper aims to critically analyze the ethical ramifications and privacy implications associated with the deployment of AWS cloud-based AI solutions within the financial services ecosystem. It will examine select case studies from the sector, identify best practices in the implementation of these technologies, and provide strategic recommendations to effectively mitigate the associated risks.
Keywords: AWS, Data Privacy, Financial Services, bias mitigation, cloud AI, data security, ethical AI, machine learning, regulatory compliance, transparency in AI