Enhancing Microservice Scalability with Serverless Architectures: An Analysis of AWS Lambda and Google Cloud Functions

Thus, today, microservices as a distinct architectural style for the construction of applications in cloud environments have become dominant. However, the conventional infrastructure management entails some issues regarding the infrastructure deployment, resources utilization, costs, and scalability. The traditional approach suggests using serverless computing, which presented a perfect solution since it provides resource management and scaling of resources depending on the current usage. The present paper aims to explain how the adoption of serverless architectures helps to improve the scalability of microservices and focuses on AWS Lambda and Google Cloud Functions. The following paper compares these two major serverless providers regarding their capabilities of scalability, performance, cost, and compatibility with a microservices architecture. Based on the experimental results on these benchmarks, the characteristics, merits, and drawbacks or each platform are compared in terms of their elasticity for handling fluctuating workloads, applications with stringent latency requirements and event-driven processing scenarios. Moreover, they presented, the pattern of microservice engineering for the AWS serverless platform, the cold-start problem, stateless and stateful microservices. As the investigation reveals, serverless computing undoubtedly delivers great benefits to micro-services’ scalability, yet the decision whether to choose AWS for Lambda or Google Cloud Functions depends on a number of factors including ecosystems compatibility, pricing strategies and types of workload. This study informs the developers and the architects who wish to build microservices based on serverless technologies as efficient, resilient, and cost-optimized solutions.