AI RESEARCH
Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework
arXiv CS.LG
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ArXi:2603.28790v1 Announce Type: cross In the emerging landscape of edge computing, the stochastic and bursty nature of serverless workloads presents a critical challenge for autonomous resource orchestration. Traditional reactive controllers, such as the Kubernetes Horizontal Pod Autoscaler (HPA), suffer from inherent reaction latency, leading to Service Level Objective (SLO) violations during traffic spikes and resource flapping during ramp-downs.