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

2026-07-16

AI Summary

This work addresses the challenges of autoscaling in serverless computing caused by dynamic workloads, cold-start latency, and inter-function dependencies. To this end, the authors propose a dependency-aware autoscaling framework that identifies critical functions through a directed dependency graph and integrates weighted degree centrality analysis with an ensemble of lightweight multi-expert models—comprising MLP, LSTM, and CNN—combined via Bayesian heuristic probability fusion to achieve high-accuracy workload forecasting. A cost- and cold-start-aware control policy is further designed to optimize resource provisioning. Experimental results demonstrate a prediction accuracy of 99.88%, substantially outperforming existing hybrid forecasting approaches, and show significant reductions in infrastructure costs across diverse cloud pricing models while meeting performance objectives.

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 as directed dependency graphs, and structurally important functions are identified using weighted degree centrality. Resource demand is predicted using lightweight MLP, LSTM, and CNN models. Their outputs are combined through a performance-weighted probabilistic ensemble inspired by Bayesian model averaging. The controller further incorporates cold-start awareness and cost comparison to select among scale-up, scale-down, and hold actions. Experiments using real workload traces show that supervised forecasting substantially outperforms unsupervised clustering for autoscaling decision generation. The proposed ensemble achieves 99.88 percent prediction accuracy and reduces prediction error compared with representative hybrid forecasting methods. Evaluations across multiple cloud pricing models also demonstrate consistent infrastructure cost reductions while maintaining performance targets. The results show that combining dependency analysis, multi-expert forecasting, and cost-aware control provides a robust and practical solution for serverless autoscaling.

Problem

• serverless computing
• autoscaling
• cold-start latency
• function dependencies
• dynamic workloads

Innovation

• dependency-aware autoscaling
• multi-expert consensus
• serverless computing
• cold-start awareness
• cost-aware control