A Cloud-Native Customer Relationship Management Platform on AWS Fargate with Automated DevOps Workflow Orchestration

Abstract: Customer relationship management systems are central to modern sales and service operations, yet many remain bound to monolithic, server-bound deployments that scale poorly, cost more than necessary during idle periods, and are slow and risky to update. This paper presents the design and evaluation of a cloud-native customer relationship management platform that decomposes core business capabilities into containerized microservices executed on a serverless container runtime, removing the burden of provisioning and managing virtual machines. The backend services are implemented in Java and exposed through a load-balanced gateway, while a Node.js client delivers an interactive console for managing customers, leads, deals, and activities. Persistent records reside in a managed relational database, frequently accessed data is cached in memory, attachments are stored in object storage, and asynchronous events propagate through a managed messaging bus. The complete software lifecycle—source control, build, image creation, and deployment—is automated through a managed delivery pipeline driven by container images, and runtime telemetry feeds automatic horizontal scaling. Experimental evaluation under synthetic concurrency shows that the platform sustains a 95th-percentile response time of approximately 128 ms at 1000 concurrent users, where a monolithic baseline on a fixed virtual machine exceeds 760 ms, and that automated delivery reduced deployment lead time from roughly 80 minutes to under 10 while lowering steady-state cost. The contributions are an integrated serverless-container reference architecture for customer relationship management, a reproducible DevOps automation strategy, and a quantitative comparison demonstrating the operational and economic benefits of the proposed approach.

Keywords: Cloud-native computing; Customer relationship management; AWS Fargate; Microservices; DevOps; Continuous delivery; Containerization; Auto-scaling