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AI Research
arXiv CS.AI·16d ago·5 min read
An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
TL;DR
Serverless environments face challenges in scaling resources efficiently. A multi-expert consensus mechanism was developed to optimize auto-scaling decisions.
✦ Why It Matters
Implement a multi-expert consensus mechanism today to optimize your serverless application's auto-scaling and reduce costs.
Key Takeaways
- Utilize directed dependency graphs for function analysis.
- Implement MLP, LSTM, and CNN for accurate workload predictions.
- Adopt a performance-weighted probabilistic ensemble for decision-making.
- Evaluate cold-start impacts and costs in scaling decisions.
How It Works
The framework represents serverless applications as directed dependency graphs, identifying critical functions using weighted degree centrality. It forecasts resource demand through a combination of lightweight machine learning models, including MLP, LSTM, and CNN.
The outputs are integrated using a performance-weighted probabilistic ensemble, which enhances prediction accuracy and informs scaling decisions based on cost and cold-start considerations.