The Impact of Prediction Models on Energy-Aware Resource Management in FaaS Platforms

Edge Function-as-a-Service is an emerging computing model that dynamically schedules function executions across distributed edge (close to users) locations to reduce latency and improve user experience. Accurate time-series prediction models, which forecast the future number of function invocations, are crucial for energy-efficient function scheduling, enabling proactive resource allocation. In this work, we evaluate the impact of different neural time-series predictors based on Gaussian processes, recurrent neural networks, and transformer architectures in forecasting the number of function invocations. Furthermore, we propose the Energy-Aware Resource Management (EA-RM) scheduling algorithm, based on a mixed-integer problem, designed to minimize overall energy consumption by reducing the number of edge nodes used.We analyze how prediction accuracy influences function scheduling with respect to energy consumption, using real-world data that include different functions and resources. Experimental results show that the transformer-based predictor outperforms the other considered predictors, leading to more precise function scheduling. Moreover, resource allocation performed through the EA-RM algorithm reduces the energy consumption by ~12−45% on average w.r.t. competitors, and is proven to be more robust w.r.t. the accuracy of the prediction model used.