Predicting FaaS Runtime with the Orama Framework Using Machine Learning - Carvalho, Leonardo Rebouças de; Rocha Filho, Geraldo Pereira; Araujo, Aleteia (Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD); 2025: Anais do XXVI Simpósio em Sistemas Computacionais de Alto DesempenhoDO - 10.5753/sscad.2025.15723, 2025)
One of the significant challenges in Function-as-a-Service (FaaS) is the unpredictability of function runtimes, which can lead to cost overruns and performance degradation when deploying applications across multiple cloud providers. This paper presents a Machine Learning (ML)-based predictor integrated into the Orama Framework, which combines static code metrics (Halstead complexity measures) with empirical performance data to estimate function runtimes directly from the source code. Three neural network architectures (Dense, LSTM, and BLSTM) were evaluated, with the BLSTM achieving the highest accuracy (R2 = 0.91) and a 20% lower MSE compared to the baselines. The predictor is available via API and through a graphical interface within the Orama Framework, supporting deployment planning, multi-cloud comparisons, and cost-performance optimization for serverless applications.