{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "trace-reduction",
  "label": "Trace Reduction",
  "title": "Trace Reduction Research",
  "description": "Research papers on reducing trace volume, event loss, and storage cost while preserving diagnostic evidence.",
  "introduction": "This topic addresses the cost of collecting, storing, and analyzing large execution traces. The papers study reduction through abstraction, filtering, adaptive collection, feature selection, or targeted instrumentation, with the individual records documenting the retained evidence and the measured trade-offs.",
  "aliases": [
    "trace volume reduction",
    "trace compression",
    "adaptive trace collection"
  ],
  "search_terms": [
    "trace reduction",
    "trace volume",
    "trace overhead",
    "adaptive tracing",
    "event loss"
  ],
  "related_topics": [
    "trace-filtering",
    "trace-abstraction",
    "kernel-tracing",
    "performance-engineering",
    "resource-analysis"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/trace-reduction.html",
  "paper_count": 6,
  "papers": [
    {
      "paper_id": "lmat-adaptive-tracing-language-models",
      "title": "LMAT: An adaptive tracing approach based on efficient system behavior analysis using language models",
      "year": 2026,
      "authors": [
        "Kasra Darvishi",
        "Morteza Noferesti",
        "Yuvraj Sehgal",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/lmat-adaptive-tracing-language-models/",
      "canonical_source_url": "https://doi.org/10.1016/j.jss.2026.112890",
      "core_contribution": "LMAT combines multi-task language models for kernel-event and event-duration prediction with online change detection, lightweight error-vector root-cause analysis, and adaptive tracing control.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "llm-assisted-analysis",
        "observability",
        "anomaly-detection",
        "root-cause-analysis",
        "trace-reduction"
      ],
      "keywords": [
        "adaptive tracing",
        "LTTng",
        "kernel events",
        "system-call sequences",
        "event-duration modeling",
        "language models",
        "LSTM",
        "BERT",
        "multi-task learning",
        "HDBSCAN",
        "change detection",
        "root-cause analysis",
        "Apache",
        "Sock Shop",
        "OpenTelemetry",
        "Prometheus",
        "stress-ng",
        "trace reduction",
        "trace overhead"
      ]
    },
    {
      "paper_id": "execution-trace-reconstruction-using-diffusion-based-generative-models",
      "title": "Execution Trace Reconstruction Using Diffusion-Based Generative Models",
      "year": 2025,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/execution-trace-reconstruction-using-diffusion-based-generative-models/",
      "canonical_source_url": "https://doi.org/10.1109/ICSE55347.2025.00063",
      "core_contribution": "The paper reconstructs missing system-call events in execution traces with diffusion and structured state-space generative models.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-analysis",
        "trace-reduction",
        "performance-analysis"
      ],
      "keywords": [
        "execution trace reconstruction",
        "trace imputation",
        "diffusion models",
        "DiffWave",
        "SSSDS4",
        "SSSDSA",
        "CSDIS4",
        "structured state-space",
        "system-call sequences",
        "LTTng",
        "Phoronix Test Suite",
        "ROUGE-L",
        "perfect rate",
        "missing events",
        "ring-buffer overflow"
      ]
    },
    {
      "paper_id": "an-adaptive-logging-system-als-enhancing-software-logging-with-reinforcement-learning-techniques",
      "title": "An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning Techniques",
      "year": 2024,
      "authors": [
        "Amirmahdi Khosravi Tabrizi",
        "Naser Ezzati-Jivan",
        "Francois Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/an-adaptive-logging-system-als-enhancing-software-logging-with-reinforcement-learning-techniques/",
      "canonical_source_url": "https://doi.org/10.1145/3629526.3645033",
      "core_contribution": "ALS uses source-code features and reinforcement learning to recommend which Python functions to log and which log levels to use for performance-bug diagnosis.",
      "tags": [
        "observability",
        "performance-engineering",
        "machine-learning",
        "trace-reduction"
      ],
      "keywords": [
        "adaptive logging",
        "ALS",
        "reinforcement learning",
        "log placement",
        "log level selection",
        "performance bugs",
        "Python AST",
        "Stable-Baselines",
        "DQN",
        "A2C",
        "PPO",
        "Apache",
        "Django",
        "cross-project evaluation",
        "Ciena",
        "source-code features"
      ]
    },
    {
      "paper_id": "automatic-reduction-execution-trace-data-volume",
      "title": "Automatic Reduction of Execution Trace Data Volume Using Gradient Boosting in Large-Scale Microservice Systems",
      "year": 2024,
      "authors": [
        "Amir Haghshenas",
        "Naser Ezzati-Jivan",
        "Michel Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/automatic-reduction-execution-trace-data-volume/",
      "canonical_source_url": "https://doi.org/10.21428/594757db.fe8b76cf",
      "core_contribution": "The paper uses gradient boosting and feature importance to reduce the amount of trace data needed for microservice performance modeling.",
      "tags": [
        "microservices",
        "trace-reduction",
        "gradient-boosting",
        "performance-modeling",
        "resource-analysis"
      ],
      "keywords": [
        "trace data volume",
        "feature importance",
        "CPU demand",
        "memory demand",
        "Alibaba microservices",
        "inter-service communication"
      ]
    },
    {
      "paper_id": "toward-adaptive-tracing-language-models",
      "title": "Toward Adaptive Tracing: Efficient System Behavior Analysis using Language Models",
      "year": 2024,
      "authors": [
        "Kasra Darvishi",
        "Morteza Noferesti",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/toward-adaptive-tracing-language-models/",
      "canonical_source_url": "https://doi.org/10.1145/3639476.3639778",
      "core_contribution": "The paper proposes an adaptive tracing loop that combines language-model prediction of kernel-event sequences and event durations with change detection and root-cause analysis, so detailed tracing is activated only around significant behavior shifts.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "llm-assisted-analysis",
        "observability",
        "anomaly-detection",
        "root-cause-analysis",
        "trace-reduction"
      ],
      "keywords": [
        "adaptive tracing",
        "LTTng",
        "kernel events",
        "system-call sequences",
        "event-duration modeling",
        "language models",
        "LSTM",
        "BERT",
        "multi-task learning",
        "change detection",
        "root-cause analysis",
        "Apache2",
        "trace reduction",
        "trace overhead"
      ]
    },
    {
      "paper_id": "dynamic-trace-sampling-memory-usage",
      "title": "Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications",
      "year": 2017,
      "authors": [
        "Houssem Daoud",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/dynamic-trace-sampling-memory-usage/",
      "canonical_source_url": "https://doi.org/10.1109/HPEC.2017.8091061",
      "core_contribution": "The paper introduces a kernel-resident dynamic sampler that tracks virtual and physical memory usage while reducing the event volume generated by high-frequency memory activity.",
      "tags": [
        "kernel-tracing",
        "resource-analysis",
        "performance-analysis",
        "trace-reduction"
      ],
      "keywords": [
        "memory usage",
        "dynamic sampling",
        "enterprise applications",
        "trace overhead",
        "LTTng",
        "kernel module",
        "Kprobes",
        "kmem_mm_page_alloc",
        "kmem_mm_page_free"
      ]
    }
  ]
}