用成本模型选硬件，便宜显卡也能抗对抗攻击
Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness
- 用加速失效时间模型量化硬件、批量大小等对模型存活时间影响
- L4显卡比V100贵75%但存活时间多20%，证明高价不等于强鲁棒性
- 推理延迟比训练时间更关键，适合云原生系统优化者参考
部署对抗鲁棒的机器学习系统需在鲁棒性、成本和延迟间持续权衡。本文提出一个自主决策支持框架，为云原生深度学习中的硬件选择与超参数调优提供量化基础。该框架采用加速失效时间（AFT）模型，量化硬件类型、批处理大小、训练轮数及验证准确率对模型生存时间的影响。该框架可自然集成至自适应控制环（监控-分析-规划-执行，MAPE-K），持续评估成本、鲁棒性和延迟等系统指标，并据此动态调整模型配置与硬件选择。在三种GPU架构上的实验验证了该框架的有效性与成本效益：Nvidia L4相比V100成本低75%的同时，使对抗生存时间提升20%，表明昂贵硬件未必带来更强鲁棒性。分析还显示，模型推理延迟是比训练时间或硬件配置更强的对抗鲁棒性预测因子。
原文摘要 · Abstract (English)
Deploying adversarially robust machine learning systems requires continuous trade-offs between robustness, cost, and latency. We present an autonomic decision-support framework providing a quantitative foundation for adaptive hardware selection and hyper-parameter tuning in cloud-native deep learning. The framework applies accelerated failure time (AFT) models to quantify the effect of hardware choice, batch size, epochs, and validation accuracy on model survival time. This framework can be naturally integrated into an autonomic control loop (monitor--analyse--plan--execute, MAPE-K), where system metrics such as cost, robustness, and latency are continuously evaluated and used to adapt model configurations and hardware selection. Experiments across three GPU architectures confirm the framework is both sound and cost-effective: the Nvidia L4 yields a 20% increase in adversarial survival time while costing 75% less than the V100, demonstrating that expensive hardware does not necessarily improve robustness. The analysis further reveals that model inference latency is a stronger predictor of adversarial robustness than training time or hardware configuration.
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