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

Bablu Kumar, Anshul Verma, Rajkumar Buyya

Sep 14, 2026arXiv:2609.14894v1
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Scorecard· 16/16
3.0/10 impact

Competent but incremental combination of off-the-shelf DDQN and a smoothing heuristic, evaluated only in a toy simulation with weak statistical rigor and marginal gains.

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.

AI Impact Assessments

(1 model)

Scientific Impact Assessment

1. Core Contribution

The paper proposes DDQN+RRS, a proactive autoscaling framework for containerized cloud applications that combines a Double Deep Q-Network (DDQN) for scaling decisions with a "Resource Removal Strategy" (RRS) — an execution-level smoothing mechanism that regulates the magnitude of pod adjustments over a 60-second control window. The central claim is that decision-level intelligence (DDQN reduces Q-value overestimation vs. DQN) plus execution-level smoothing (RRS gradually applies changes via `N_{t+1} = N_t + β(N_req − N_t)`) jointly reduce scaling oscillations, SLA violations, and pod restarts while improving CPU utilization.

The problem — oscillation and instability in reactive/threshold-based autoscalers (e.g., Kubernetes HPA) — is real and well-known. However, the core technical ingredients are entirely off-the-shelf: DDQN is a 2016 algorithm, RL-based autoscaling is a crowded field (the authors cite numerous prior works including their own advisor Buyya's deep recurrent RL work), and the RRS "smoothing" is essentially exponential-smoothing / a stabilization window, a well-established idea reframed with new terminology. The novelty is thus a specific combination rather than a conceptual advance.

2. Methodological Rigor

This is the weakest aspect. Several serious concerns:

  • The evaluation is a simplified simulation, not a real deployment. The authors admit this in the conclusion: seven "logical nodes" and pods are abstractions in a Python script run on Google Colab. Pod startup latency, image pulls, scheduling delays, and network effects — precisely the factors that make autoscaling stability hard — are absent.
  • Workload realism is questionable. The "dataset" is a 5,000-record Kaggle CSV (with a `utm_source=chatgpt.com` URL suggesting it was found via ChatGPT), aggregated into 30-second bins. This is a thin, non-standard workload trace, not an established benchmark (e.g., Alibaba, Google Borg, Wikipedia traces).
  • No statistical rigor. No error bars, no confidence intervals, no repeated seeds (a single random seed 42 is fixed), no significance testing. Reported differences (e.g., 52.23% vs. 51.08% CPU utilization) are tiny and could be noise.
  • Internally inconsistent numbers. Figure 3 reports reactive scaling frequency of 3139 and SLA 19.68%, while Figure 4/Table VI report reactive at 2974 events and 13.21% SLA — the same "reactive" baseline yields different numbers across sections, undermining confidence.
  • Table V's reward values appear hand-constructed/illustrative rather than measured.
  • Ablation is present and is the paper's most useful element (DDQN-alone actually produces the *worst* scaling frequency at 3785, showing RRS carries most of the benefit) — but this also suggests the DDQN component adds little, and a simple smoothing filter on any controller might suffice.

    • 3. Potential Impact

    Low. The field of RL-based cloud autoscaling is mature and competitive, and this paper does not advance the state of the art in a demonstrable way — it uses an older algorithm (DDQN rather than PPO/SAC/actor-critic methods now standard), evaluates in a toy simulation, and reports marginal improvements on a non-standard trace. Practitioners deploying Kubernetes autoscalers would find nothing directly usable, since the framework was never run on a real cluster. The RRS smoothing idea is intuitively sensible and could inform a heuristic, but it is not a novel primitive. It is unlikely to be widely cited beyond incremental follow-ups.

    4. Timeliness & Relevance

    The topic is relevant — proactive, stability-aware autoscaling for microservices/edge is an active area, and the co-authorship of Rajkumar Buyya (a prominent cloud-computing figure) lends visibility. But the execution lags the field's methodological frontier. The arXiv metadata lists a 2026 date; by that point DDQN is dated relative to current DRL practice, and stability-aware autoscaling has been addressed by more sophisticated methods.

    5. Strengths & Limitations

    Strengths:

  • Clear articulation of a genuine problem (scaling oscillation).
  • The decision/execution separation is a reasonable framing, and the ablation isolates RRS's contribution honestly.
  • Reasonably well-written and organized, with explicit algorithms and a detailed config table (aiding reproducibility of the simulation).
  • Authors are candid about the simulation-only limitation.

    • Limitations:

  • Simulation-only, non-standard thin dataset, no real Kubernetes validation.
  • No statistical treatment; inconsistent baseline numbers across figures.
  • Marginal quantitative gains (~1–3 percentage points on utilization).
  • Off-the-shelf components; limited conceptual novelty.
  • Comparison baselines are re-implemented by the authors under their own simplified environment, not against published systems.
  • The ablation implies DDQN itself contributes little beyond the smoothing heuristic.

    • Overall

    This is a competently written but incremental engineering paper that combines a known DRL algorithm with a known smoothing idea, evaluated in a simplified simulation with weak statistical rigor and a non-standard workload. It addresses a real problem but does not convincingly advance the state of the art, and its impact is likely to be modest — a citation in related-work surveys and perhaps a base for the authors' own future (promised) real-cluster follow-up.

    Rating:3/ 10
    Significance 3Rigor 3Novelty 3Clarity 6.5

    Generated Sep 15, 2026

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