闭环：持续测量驱动的卸载预测精化
Closing the Loop: Continuous Measurement-Driven Refinement of Offloading Predictions
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中文总结 AI 辅助
针对车辆卸载预测漂移问题，提出测量驱动的在线闭环校准方法，通过增量更新多头神经网络减少漂移，提升RTT等指标预测准确性，并指出多模态分布带来的预测极限。
中文摘要 AI 辅助
现代车辆越来越多地将计算密集型的感知和决策功能卸载到后端服务器，这需要对往返时间（RTT）、处理时间和利用率等绝对性能指标进行准确预测。在实践中，强烈的时间变异性、异构的后端硬件以及多模态延迟机制导致离线训练的预测器发生漂移，从而对延迟敏感的功能造成可靠性缺口。我们通过一个操作性的、测量驱动的闭环来解决这一缺口，该闭环在运行时持续重新校准绝对值预测器。该系统将实际执行测量与预测值对齐，并在保持模型稳定性的同时，对轻量级多头神经网络进行增量式在线更新。该模型隐式地学习了输入指标的广泛非高斯分布，我们评估中的基于sigma的误差分析刻画了动态条件下的残差变异性。在两个Kubernetes集群上的实验表明，持续的测量驱动精化减少了预测漂移，提高了RTT、处理时间和利用率的准确性，并稳定了跨异构延迟机制的预测行为。然而，输入指标的广泛且多模态的分布对绝对值预测施加了根本性限制，残差误差经常超过配置的阈值。总体而言，在线校准在动态车辆边缘环境中对于稳健的计算卸载是可行且必要的，同时强调了未来需要解决极端延迟机制和高方差运行条件的机制。
英文摘要
Modern vehicles increasingly offload computation- ally intensive perception and decision functions to backend servers, requiring accurate predictions of absolute performance metrics such as Round-Trip Time (RTT), processing time, and utilization. In practice, strong temporal variability, heterogeneous backend hardware, and multimodal latency regimes cause offline- trained predictors to drift, creating a reliability gap for latency- sensitive functions. We address this gap with an operational, measurement-driven closed loop that continuously recalibrates absolute-value predictors during runtime. The system aligns real execution measurements with predicted values and performs incremental online updates of a lightweight multi-head neural network while preserving model stability. The model implicitly learns the broad, non-Gaussian spread of input metrics, and a sigma-based error analysis in our evaluation characterizes resid- ual variability under dynamic conditions. Experiments across two Kubernetes clusters show that continuous measurement- driven refinement reduces prediction drift, improves accuracy for RTT, processing time, and utilization, and stabilizes prediction behavior across heterogeneous latency regimes. However, the broad and multimodal distribution of input metrics imposes fundamental limits on absolute-value prediction, with residual errors frequently exceeding configured thresholds. Overall, online calibration proves feasible and necessary for robust computation offloading in dynamic vehicular edge environments, while high- lighting the need for future mechanisms that address extreme latency regimes and high-variance operating conditions.
发表机构
- Institute of Industrial Automation and Software Engineering (IAS) University of Stuttgart（斯图加特大学工业自动化与软件工程研究所）
机构由 AI 辅助整理，请以论文原文为准。
