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

arXiv cs.AI Papers
serverless auto-scaling multi-expert consensus dependency-analysis cold-start cloud-computing

Summary

This paper presents a dependency-aware autoscaling framework for serverless environments, integrating graph-based bottleneck identification, multi-model forecasting (MLP, LSTM, CNN) via a probabilistic ensemble, and cost-aware scaling control. Experiments show 99.88% prediction accuracy and reduced infrastructure costs.

arXiv:2607.15511v1 Announce Type: cross 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.
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# An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
Source: [https://arxiv.org/abs/2607.15511](https://arxiv.org/abs/2607.15511)
[View PDF](https://arxiv.org/pdf/2607.15511)

> 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\.

## Submission history

From: Mobina Kashaniyan \[[view email](https://arxiv.org/show-email/42728dc7/2607.15511)\] **\[v1\]**Thu, 16 Jul 2026 23:48:37 UTC \(3,566 KB\)

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