用扩散模型生成高质量内核执行轨迹，解决真实数据难获取问题。
TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models
- 基于扩散模型建模多通道内核轨迹，结合约束修复保持系统一致性。
- 在计算密集型任务中，合成数据达87.2%的F1-Macro，仅比真实数据低2.6个百分点。
- 轻量2通道模型性能接近全模型，计算成本减半，适合生产环境部署。
系统诊断的机器学习模型依赖内核执行轨迹来捕捉细粒度行为，但工业系统中收集真实生产轨迹因运行开销、存储需求和隐私限制而成本高昂。本文提出TraceSynth，一种基于扩散模型的合成内核轨迹生成框架，用于增强有限的真实数据以支持下游机器学习任务。TraceSynth将轨迹建模为多通道序列（事件类型、时间戳、CPU亲和性、线程标识符、进程元数据），采用Transformer-based去噪扩散过程，并引入约束引导修复以保证系统不变性。在六个基准测试中，结果表现出显著的工作负载依赖性：对于确定性的计算密集型工作负载（scimark2），合成数据在上下文长度L=4096时达到87.2% F1-Macro，仅比纯真实数据基线低2.6个百分点；上下文长度是质量主导因素，L=4096相比L=256提升104%相对性能，约束修复使合成数据质量最高提升4.3%。消融实验表明，轻量2通道模型在约一半计算成本下保留了97-99%的全6通道模型性能。TraceSynth支持生产可观测性流水线中的低成本轨迹数据增强，并帮助判断合成数据是否可替代有限真实数据。
原文摘要 · Abstract (English)
Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints. We present TraceSynth, a diffusion-based framework for generating synthetic kernel traces that augment limited real data for downstream ML tasks. TraceSynth models traces as multi-channel sequences (event types, timestamps, CPU affinity, thread identifiers, and process metadata) using a Transformer-based denoising diffusion process with constraint-guided repair to enforce system invariants. Across six benchmarks, results show strong workload dependence. For deterministic, compute-heavy workloads (scimark2), synthetic augmentation achieves 87.2% F1-Macro at context length L=4096, only 2.6 percentage points below real-only baselines. Context length is the dominant quality factor, with L=4096 yielding a +104% relative improvement over L=256, while constraint-guided repair improves synthetic data quality by up to 4.3%. Ablation studies show that lightweight 2-channel models retain 97-99% of the performance of full 6-channel models at roughly half the computational cost. TraceSynth supports cost-effective augmentation of kernel execution traces in production observability pipelines and helps identify when synthetic data can substitute for limited real traces.
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