An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism

arXiv:2607.15511v1 Announce Type: new Abstract: Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because of dynamic workloads, cold-start latency, and dependencies among functions. We present a dependency-aware autoscaling framework that integrates graph-based bottleneck identification, short-term workload forecasting, multi-model consensus, and cost-aware scaling control. Serverless applications are represented ...