An auto-scaling approach for serverless environments based on a multi-expert consensus mechanism

Serverless computing offers automatic resource management and pay-per-use execution, but autoscaling remains difficult due to cold-start latency, inter-function dependencies, and highly dynamic workloads. Many existing approaches scale functions independently or rely on a single predictor, which can reduce robustness and cost efficiency. We present a dependency-aware autoscaling framework that unifies bottleneck identification, short-horizon demand forecasting, and cost-aware control in an end-to-end pipeline. We model applications as directed dependency graphs and prioritize high-impact functions using degree centrality. For these bottlenecks, near-term demand is predicted using lightweight supervised models, whose outputs are fused via a performance-weighted probabilistic ensemble inspired by Bayesian model averaging to improve stability under workload variability. The controller also accounts for cold starts and filters candidate actions through a cost-comparison mechanism to balance latency and operational efficiency. Experiments on real workload traces show improved prediction accuracy and more stable scaling decisions than representative baselines; supervised forecasting also consistently outperforms unsupervised clustering for generating autoscaling actions. The primary contribution is a practical system-level design that integrates dependency analysis, ensemble-based prediction, and cost-aware decision-making for robust serverless autoscaling.