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Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling

Shaikh, Faraz et al.
arXiv (All) · Papers
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and-cluster-computingdistributedmachine-learningparallel
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
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