Cloud Computing Optimization Using Serverless Architecture for Scalable Applications

Abstract

Cloud computing has revolutionized the way computational resources are provisioned and consumed, allowing organizations to focus on application development rather than infrastructure management. Traditional cloud-based architectures often rely on virtual machines and container-based solutions, which incur overhead in terms of maintenance and scalability limitations. This paper investigates the design and implementation of serverless computing models as an optimized alternative for deploying scalable cloud applications. The study presents a comprehensive architecture where Function-as-a-Service (FaaS) platforms, such as AWS Lambda and Google Cloud Functions, manage computing workloads without the need for manual server provisioning. We explore the architectural considerations, including event driven triggers, stateless function design, and resource limitations imposed by serverless platforms. Using a case study application involving real-time data processing, we benchmark the serverless approach against traditional microservices-based deployment. Our results indicate that serverless architecture reduces operational costs by up to 40%, scales automatically to handle bursts of traffic, and significantly lowers development complexity. Additionally, performance measurements show marginal latency overhead, which is acceptable in most application scenarios. The research provides best practices and insights into when and how to apply serverless paradigms for optimal cost-performance trade-offs in cloud environments.

KEYWORDS: Cloud Computing, Serverless Architecture, FaaS, Scalability, Cost Optimization