Multi-Expert Consensus Framework Enhances Serverless Autoscaling with Cost and Dependency Awareness
A new autoscaling framework for serverless environments combines graph-based dependency analysis, short-term workload forecasting using multiple neural models (MLP, LSTM, CNN), and cost-aware scaling control. The approach uses a probabilistic ensemble of predictors, achieving 99.88% prediction accuracy and reducing infrastructure costs while maintaining performance targets in experiments with real workload traces. The framework also incorporates cold-start awareness and evaluates performance across multiple cloud pricing models.
Why it matters: This research demonstrates a robust and practical advance in serverless autoscaling, addressing key challenges of workload prediction, cost efficiency, and dependency management in cloud applications.
Full story at: arXiv AI/ML ↗