An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
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-weigh...
| Authors | Mobina Kashaniyan, Mehrdad Ashtiani, Amirhossein Ghassemi |
|---|---|
| Primary category | cs.LG |
| Published | 16 July 2026 |
| Last updated at source | 10 August 2026 |
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