From Source Code to Cost: A Multilingual, Cost-Aware Runtime Prediction Framework for Multi-Cloud FaaS

Abstract

Predicting the runtime and cost of Function-as-a-Service (FaaS) applications remains challenging in multi-cloud environments due to variations in code complexity, workload characteristics, and provider-specific behaviors. This paper presents an extended version of the Orama Framework that advances runtime prediction toward a multilingual and cost-aware approach. The framework incorporates a multilingual Halstead metric extractor for language-agnostic static analysis and enhances the predictor to estimate execution costs by combining runtime forecasts with cloud pricing models across AWS Lambda, Google Cloud Functions, Azure Functions, and Alibaba Function Compute. To assess robustness and generalization, the data set is expanded with a scientific workload based on genetic sequence alignment, introducing input-sensitive execution patterns. The extended data set integrates static code metrics, workload scale, infrastructure metadata, and empirical multi-cloud execution traces. Neural network models (Dense, LSTM, and BLSTM) are retrained and evaluated using standard regression metrics and cost estimation. Results indicate that the enhanced BLSTM model maintains high predictive precision across heterogeneous workloads and providers, while enabling cross-cloud cost estimation directly from source code. The extended framework provides a unified approach for performance and cost-aware prediction in serverless computing environments.