Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework

2026-03-21

arXiv.org

AI Summary

为解决边缘计算中无服务器工作负载的突发性和随机性导致的资源调度问题，提出了一种结合注意力机制增强的双层LSTM架构与PPO算法的自动扩展框架。

Abstract

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. While Deep Reinforcement Learning (DRL) offers a pathway toward proactive management, standard agents suffer from temporal blindness, an inability to effectively capture long-term dependencies in non-Markovian edge environments. To bridge this gap, we propose a novel stability-aware autoscaling framework unifying workload forecasting and control via an Attention-Enhanced Double-Stacked LSTM architecture integrated within a Proximal Policy Optimization (PPO) agent. Unlike shallow recurrent models, our approach employs a deep temporal attention mechanism to selectively weight historical states, effectively filtering high-frequency noise while retaining critical precursors of demand shifts. We validate the framework on a heterogeneous cluster using real-world Azure Functions traces. Comparative analysis against industry-standard HPA, stateless Double DQN, and a single-layer LSTM ablation demonstrates that our approach reduces 90th percentile latency by approximately 29% while simultaneously decreasing replica churn by 39%, relative to the single-layer LSTM baseline. These results confirm that mitigating temporal blindness through deep attentive memory is a prerequisite for reliable, low-jitter autoscaling in production edge environments.

Problem

• edge computing
• serverless workloads
• resource orchestration
• Service Level Objective (SLO) violations
• reaction latency

Innovation

• Attention-Enhanced Double-Stacked LSTM
• Proximal Policy Optimization (PPO)
• Temporal Blindness
• Edge Computing
• Autoscaling