Enhancing Performance Modeling of Serverless Functions via Static Analysis

Serverless computing leverages the design of complex applications as the composition of small, individual functions to simplify development and operations. However, this flexibility complicates reasoning about the trade-off between performance and costs, requiring accurate models to support prediction and configuration decisions. Established performance model inference from execution traces is typically more expensive for serverless applications due to the significantly larger topologies and numbers of parameters resulting from the higher fragmentation into small functions. On the other hand, individual functions tend to embed simpler logic than larger services, which enables inferring some structural information by reasoning directly from their source code. In this paper, we use static control and data flow analysis to extract topological and parametric dependencies among interacting functions from their source code. To enhance the accuracy of model parameterization, we devise an instrumentation strategy to infer performance profiles driven by code analysis. We then build a compact layered queueing network (LQN) model of the serverless workflow based on the static analysis and code profiling data. We evaluated our method on serverless workflows with several common composition patterns deployed on Azure Functions, showing it can accurately predict the performance of the application under different resource provisioning strategies and workloads with a mean error under 7.3%.