Comparing Serverless Computing Platforms : A Performance and Cost Analysis

Sammanfattning

Background: Serverless computing, primarily Function as a Service (FaaS), is transforming cloud computing by allowing developers to focus on application logic without managing underlying infrastructure. While this model offers potential cost savings and rapid deployment, challenges remain concerning its performance and cost efficiency. Objectives: This thesis aims to analyze the trade-offs between performance and cost across two major serverless platforms: AWS Lambda and Azure Functions. Specifically, it investigates how different configurations (e.g., memory allocation) and workload characteristics affect performance and cost, providing actionable insights into optimizing serverless applications. Methods: A mixed-method approach was used to explore these dynamics. Quantitative experiments were conducted to measure the impact of configurations across the platforms. The configurations tested include memory allocations, and invocation frequencies. In parallel, surveys were administered to gather qualitative insights from cloud architects and developers, offering perspectives on practical optimization strategies and real-world challenges. Results: The findings indicate that optimization of serverless configurations involves a complextrade-off between cost efficiency and performance, influenced by workload types and provider-specific factors. AWS and Azure Cloud exhibited distinct performance and cost patterns depending on the workload. For instance, higher memory allocations generally led to better performance but increased costs, while resource-constrained settings significantly impacted latency-sensitive applications. Conclusions: This study contributes to understanding the performance-cost relationship in serverless computing, highlighting specific optimization strategies suitable for each cloud provider. The results suggest that while each platform has strengths, careful workload specific configurations are essential for achieving an optimal balance between cost and performance.