EdgeCloudForge: Simulation-Driven Synthetic Dataset Generation For Proactive Serverless Edge Function Autoscaling

Abstract

Serverless edge computing promises autonomous function management across the heterogeneous edge-cloud continuum. Specifically, autoscaling of functions increases resource efficiency by creating and destroying instances on demand. However, reactive function instance creation can cause high end-to-end latency due to requests waiting for processing. Researchers and practitioners use proactive autoscaling mechanisms to overcome this by predicting the required number of function instances in advance. Proactive approaches pose challenges because prediction models must be fine-tuned for new environments and updated over time. Therefore, we propose a simulation-driven framework, EdgeCloudForge, that generates datasets to train prediction models for autoscaling in edge-cloud environments ahead of their deployment, thus preparing them in advance for new environments. We introduce the concept of Edge-Cloud Domain Space to describe different infrastructure attributes. EdgeCloudForge uses a two-step method based on initial sampling and Bayesian optimization to navigate this domain space. Simulation results show that our approach can reduce SLO violations by up to 79% across different request patterns compared to the reactive approach and is able to outperform approaches from related work by up to 91%. EdgeCloudForge also shows the capability of training models that can be used across different infrastructures.