Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling

arXiv:2603.28790 · cs.DC, cs.LG · Submitted 2026-03-21 · Read on arXiv

cs.DC, cs.LG

Submitted: 2026-03-21

Updated: 2026-09-23

Code: https://github.com/Azure/AzurePublicDataset

Project page: https://ai-ran.org

Terminology

• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Trace — 13× in this paper · explained in 2 episodes
• Attention Mechanism — 11× in this paper · explained in 2 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Q-learning — 8× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 6× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Actor — 4× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Action Space — 3× in this paper · explained in 1 episode
• Decoupling — 3× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 3× in this paper · explained in 2 episodes
• Hysteresis — 3× in this paper · explained in 3 episodes
• Proximal Policy Optimization — 3× in this paper
• Reward Function — 3× in this paper · explained in 2 episodes
• Service Level Objective — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Internet of Things — 2× in this paper
• Kubernetes Horizontal Pod Autoscaler — 2× in this paper
• Learning Rate — 2× in this paper
• Markov Decision Process — 2× in this paper
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• While Deep Reinforcement Learning — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Captures the Quality of Experience — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cumulative Distribution Function — 1× in this paper
• Deep Q-Networks — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Long Short-Term Memory — 1× in this paper
• Machine Learning — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 10 episodes
• Mobile Edge Computing — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• Quality of Service — 1× in this paper
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 24 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Symposium on Edge Computing — 1× in this paper

Sources

• Auto-scaling Approaches for Microservice Applications: A Survey and Taxonomy
• An SLO Driven and Cost-Aware Autoscaling Framework for Kubernetes
• A Hybrid Reactive-Proactive Auto-scaling Algorithm for SLA-Constrained Edge Computing
• Proximal Policy Optimization Algorithms

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