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arXiv Machine Learning By Faraz Shaikh, Gianluca Reali, Mauro Femminella

Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling

Read the original on arXiv Machine Learning →

The paper introduces a stability‑aware autoscaling framework for edge serverless workloads that combines an Attention‑Enhanced Double‑Stacked LSTM with Proximal Policy Optimization to address temporal blindness in deep reinforcement learning. By weighting recent historical states non‑uniformly, the method suppresses high‑frequency jitter while preserving demand trends, outperforming single‑layer LSTM, static HPA, and KEDA baselines in latency reduction and stability. Experiments on two Kubernetes clusters with real Azure Functions traces show a ~67% reduction in P90 latency and improved adherence to a 50 ms hard SLO.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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