{
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  "paper_id": "enhancing-performance-engineering-with-kernel-events",
  "page_url": "https://threadslab.org/research-publications/papers/enhancing-performance-engineering-with-kernel-events/",
  "title": "Enhancing empirical software performance engineering research with kernel-level events: A comprehensive system tracing approach",
  "title_variants": [],
  "authors": [
    "Morteza Noferesti",
    "Naser Ezzati-Jivan"
  ],
  "author_details": [
    {
      "name": "Morteza Noferesti",
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    {
      "name": "Naser Ezzati-Jivan",
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    }
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  "publication": {
    "year": 2024,
    "venue": "Journal of Systems and Software",
    "type": "journal article",
    "publication_date": "2024-10",
    "online_date": null,
    "print_date": "2024-10",
    "volume": "216",
    "issue": null,
    "pages": "112117",
    "article_number": "112117",
    "publisher": "Elsevier BV",
    "issn": [
      "0164-1212"
    ],
    "isbn": [],
    "crossref_type": "journal-article"
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  "canonical_source_url": "https://doi.org/10.1016/j.jss.2024.112117",
  "source_record_id": "enhancing-empirical-software-performance-engineering-research-with-kernel-level-events-a-compreh-5d50f10edf",
  "identifiers": {
    "doi": "10.1016/j.jss.2024.112117"
  },
  "abstract": null,
  "abstract_source": "Official ScienceDirect abstract and public article page reviewed; author abstract not reproduced because reuse permission for the version of record was not established.",
  "abstract_available": false,
  "scholar_eligibility": {
    "eligible": false,
    "basis": "not-eligible",
    "note": "The page is a discovery record; it does not claim Google Scholar article-host eligibility."
  },
  "description": "The paper releases a reusable Linux kernel-event and system-call artifact with controlled application workloads, injected resource noise, and analysis scenarios for software performance engineering.",
  "evidence_level": "full-text-reviewed",
  "evidence": {
    "source_basis": "full-text-reviewed",
    "coverage": "material paper sections",
    "summary_origin": "AI-assisted catalog editorial summary",
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    "verified_on": "2026-08-26",
    "sources": [
      {
        "note": "Local PDF reviewed, including artifact composition, environment, noise workloads, event counts, and applications"
      },
      {
        "note": "ScienceDirect article: https://www.sciencedirect.com/science/article/pii/S0164121224001626"
      },
      {
        "note": "Author artifact repository: https://github.com/mnoferestibrocku/dataset-repo"
      }
    ]
  },
  "summary": {
    "core_contribution": "The paper releases a reusable Linux kernel-event and system-call artifact with controlled application workloads, injected resource noise, and analysis scenarios for software performance engineering.",
    "problem": "Application-level metrics can show degradation without exposing scheduling, I/O, network, memory, and interrupt behavior. Performance-engineering research therefore needs realistic trace data and reproducible noise conditions rather than isolated benchmark numbers.",
    "method": "The artifact records Linux kernel events and system calls in an Elasticsearch/Kibana-oriented workflow, with light- and heavy-load scenarios and CPU, I/O, network, and memory noise injection. The paper describes use for performance monitoring, noise detection/root-cause analysis, and software-phase detection. The evaluated environment includes Ubuntu 22.04, OpenJDK 18, LTTng 2.13.9, and Trace Compass 8.2.0; the collected artifact contains 24,263,691 events in a 924 MB trace, with sched_switch representing approximately 16.2% of events. Workloads use stress-ng CPU matrix, I/O, socket, and memory options, plus Edge-IIoTset-derived activity.",
    "findings": "The artifact provides light/heavy execution conditions and several resource-noise dimensions that can be reused to test monitoring, anomaly/root-cause analysis, and phase-detection methods. The paper's detailed counts make the collection concrete, but the artifact should be cited as an evidence/benchmark resource rather than as proof that one detector dominates alternatives. The paper's setup references Elasticsearch 6 in one place while linked materials reference 8.8; this is a reproducibility issue worth retaining.",
    "limitations": "The workloads and noise injections represent selected Linux conditions and may not cover other kernels, hardware, applications, or production interference. Event volume and instrumentation overhead require independent measurement for each use case, and the artifact does not by itself provide ground-truth root causes for every event sequence.",
    "future_work": "Add heterogeneous hardware and kernels, richer ground-truth labels, standardized trace schemas, controlled overhead studies, container/orchestrator workloads, and reproducible baseline analyses that quantify how much the artifact improves generalization."
  },
  "tags": [
    "kernel-tracing",
    "system-tracing",
    "performance-engineering",
    "observability",
    "anomaly-detection"
  ],
  "keywords": [
    "LTTng",
    "Linux kernel events",
    "system calls",
    "kernel tracepoints",
    "Elasticsearch",
    "Kibana",
    "IoT dataset",
    "stress-ng",
    "CPU noise",
    "I/O noise",
    "network noise",
    "memory noise",
    "software phase detection",
    "24,263,691 events"
  ],
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      "relation": "version-of-record",
      "title": "Enhancing empirical software performance engineering research with kernel-level events: A comprehensive system tracing approach",
      "url": "https://doi.org/10.1016/j.jss.2024.112117",
      "pdf_url": null,
      "status": "published",
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  "access": {
    "status": "published_metadata_record",
    "note": "The DOI is the canonical citation target; the authors' public artifact repository provides reproducibility resources and is linked separately.",
    "license": null
  },
  "resources": {
    "code": null,
    "data": "https://github.com/mnoferestibrocku/dataset-repo",
    "slides": null,
    "demo": null
  },
  "citation_guidance": {
    "when_to_cite": "Cite this paper when you need a reusable Linux kernel-event artifact with injected resource noise for performance-engineering evaluation.",
    "points": [
      "The light/heavy workload and CPU/I/O/network/memory noise design.",
      "The 24,263,691-event, 924 MB artifact and its analysis applications.",
      "The linked dataset repository and reproducibility materials.",
      "The selected Linux environment and version-consistency caveats."
    ],
    "canonical_version_id": "published-version"
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  "provenance": {
    "metadata_verified_on": "2026-08-26",
    "metadata_source": [
      "Local PDF reviewed, including artifact composition, environment, noise workloads, event counts, and applications",
      "ScienceDirect article: https://www.sciencedirect.com/science/article/pii/S0164121224001626",
      "Author artifact repository: https://github.com/mnoferestibrocku/dataset-repo"
    ],
    "summary_written_by": "AI-assisted",
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  "batch": {
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    "batch_label": "expanded forty-paper release",
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    "selected_at": "2026-08-28"
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}