Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments

2026-09-13

AI Summary

针对云环境中动态工作负载导致的响应延迟和缩放振荡问题，提出了一种基于双深度Q网络（DDQN）结合资源移除策略（RRS）的主动自动扩展方法，提高了稳定性和适应性。

Abstract

Dynamic workloads and latency-sensitive applications require efficient autoscaling in cloud computing environments. However, most existing approaches rely on reactive mechanisms based on static thresholds, resulting in delayed responses and scaling oscillations under workload uncertainty. To address these limitations, we propose a double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS). The proposed (DDQN+RRS) enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling. Experimental results demonstrate that the proposed method outperforms both reactive and existing proactive approaches. Specifically, DDQN+RRS achieves a lower Service Level Agreement (SLA) violation rate (11.81%), higher CPU utilization (52.23%), improved scaling stability, fewer scaling events (2,488), and reduced pod restarts (1,246). Furthermore, the approach ensures smoother autoscaling behavior by significantly reducing oscillations over time (0-60 s). While reactive methods exhibit substantial fluctuations in pod allocation, Reactive reduces these variations, and DDQN+RRS achieves the most stable and smooth scaling, particularly during the 15-30 s, 40-45 s, and 55-60 s intervals.

Problem

• dynamic workloads
• latency-sensitive applications
• efficient autoscaling
• reactive mechanisms
• static thresholds

Innovation

• Double Deep Q-Network
• Proactive Autoscaling
• Resource Removal Strategy
• Stability