用核方法精准识别性能数据的稳态起点，提升基准测试可靠性。
A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
- 结合核方法与统计检验，基于滑动窗口在线检测稳态变化。
- 相比现有方法，总误差降低14.5%，在噪声数据中表现更稳定。
- 适合需要高精度基准测试的系统性能评估场景。
本文针对性能指标时间序列中从预热阶段过渡到稳态阶段的准确检测问题，提出一种新方法。该方法借鉴化工反应器领域的技术，通过核基步骤检测与统计方法结合，实现在线稳态识别。采用滑动窗口机制，可在噪声大或不规则的时间序列中提供更精确的相变点定位。实验表明，该方法相较当前最优方法总误差降低14.5%，显著提升了稳态起始点检测的可靠性与精度。对于用户而言，该方法增强了性能基准测试的准确性与稳定性，能高效处理多样化的时序数据。其鲁棒性与适应性使其成为真实场景下性能评估的重要工具，确保结果的一致性与可复现性。
原文摘要 · Abstract (English)
This paper addresses the challenge of accurately detecting the transition from the warmup phase to the steady state in performance metric time series, which is a critical step for effective benchmarking. The goal is to introduce a method that avoids premature or delayed detection, which can lead to inaccurate or inefficient performance analysis. The proposed approach adapts techniques from the chemical reactors domain, detecting steady states online through the combination of kernel-based step detection and statistical methods. By using a window-based approach, it provides detailed information and improves the accuracy of identifying phase transitions, even in noisy or irregular time series. Results show that the new approach reduces total error by 14.5% compared to the state-of-the-art method. It offers more reliable detection of the steady-state onset, delivering greater precision for benchmarking tasks. For users, the new approach enhances the accuracy and stability of performance benchmarking, efficiently handling diverse time series data. Its robustness and adaptability make it a valuable tool for real-world performance evaluation, ensuring consistent and reproducible results.
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