Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling
In edge computing, the stochastic and bursty nature of serverless workloads challenges autonomous resource orchestration. Traditional reactive controllers, such as the Kubernetes Horizontal Pod Autoscaler (HPA), suffer from reaction latency, leading to Service Level Objective (SLO) violations during traffic spikes and resource flapping during ramp-downs. While Deep Reinforcement Learning (DRL) offers a pathway toward proactive management, standard agents suffer from \textit{temporal blindness}, an inability to exploit the recent temporal context in non-Markovian edge environments. To bridge this gap, we propose a stability-aware autoscaling framework unifying short-horizon temporal contex...
| Authors | Faraz Shaikh, Gianluca Reali, Mauro Femminella |
|---|---|
| Primary category | cs.DC |
| Published | 21 March 2026 |
| Last updated at source | 23 September 2026 |
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