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

The provided search context does not contain information regarding the paper "Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments" (arXiv:2609.14894v1).

However, based on the available context, stability-aware proactive autoscaling in cloud environments is increasingly achieved through hybrid frameworks that integrate Deep Reinforcement Learning (DRL) with predictive forecasting models. Key approaches include:

• NimbusGuard: A framework using a Deep Q-Network (DQN) agent augmented by a Long Short-Term Memory (LSTM) forecaster to predict workloads and an optional Large Language Model (LLM) for validation. It employs a multi-objective reward system that balances Quality of Service (QoS) with stability penalties to discourage volatile scaling actions.
• Attention-Double-LSTM Framework: This approach mitigates "temporal blindness" in bursty edge environments by integrating an Attention-enhanced Double-Stacked LSTM architecture directly into a Proximal Policy Optimization (PPO) control loop, unifying forecasting and control for superior stability compared to value-based methods like DQN.
• Drift-Aware Mechanisms: Recent taxonomies highlight the use of Autoscaling Drift Index (ADI) and uncertainty-aware correction loops to refine proactive predictions, ensuring robust performance in heterogeneous and federated cloud-edge systems.

Traditional reactive methods like the Horizontal Pod Autoscaler (HPA) often lag behind dynamic demands, leading to under-provisioning or resource waste, whereas these proactive DRL-based systems aim to anticipate load spikes and optimize resource allocation preemptively.

This arXiv preprint addresses autoscaling in cloud environments where workloads are dynamic and applications are latency-sensitive. It argues that conventional autoscaling is often reactive, relying on static utilization or request-rate thresholds that trigger scaling only after performance degradation has already begun. Under uncertain or bursty workloads, such mechanisms can produce delayed responses, unnecessary scaling churn, and oscillatory behavior. The paper proposes a stability-aware proactive autoscaling approach based on a double deep Q-network, treating resource scaling as a sequential decision problem in which the controller learns when to scale up, scale down, or hold steady based on observed system and workload signals.

The key contribution is the use of a reinforcement-learning policy that explicitly balances performance objectives with stability. A double DQN architecture is relevant here because it reduces the overestimation bias common in standard DQN-based control, making the learned scaling policy more robust when applied to noisy cloud metrics. The “stability-aware” aspect suggests that the reward or constraint formulation is not only concerned with meeting latency or utilization targets, but also with avoiding excessive scaling actions, resource thrashing, and oscillations. In effect, the work shifts autoscaling from threshold-triggered correction toward learned, anticipatory control that can smooth out resource changes while still responding to workload shifts.

This matters because autoscaling is a core operational challenge in cloud computing, where poor scaling decisions can simultaneously hurt user experience, increase infrastructure cost, and destabilize services. A learned, stability-aware controller could be especially valuable for environments with highly variable traffic, such as web services, microservices, and latency-sensitive SaaS workloads. By targeting both responsiveness and oscillation suppression, the paper contributes to a more adaptive and cost-effective alternative to rule-based autoscalers, particularly in settings where static thresholds are difficult to tune and workload patterns are nonstationary.