{"ID":6621247,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12104","arxiv_id":"2607.12104","title":"TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models","abstract":"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.","short_abstract":"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 sy...","url_abs":"https://arxiv.org/abs/2607.12104","url_pdf":"https://arxiv.org/pdf/2607.12104v1","authors":"[\"Yuvraj Sehgal\",\"Sneh Patel\",\"Mahsa Panahandeh\",\"Naser Ezzati-Jivan\",\"Francois Tetreault\"]","published":"2026-07-13T19:25:26Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.LG\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}