{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "performance-modeling",
  "label": "Performance Modeling",
  "title": "Performance Modeling Research",
  "description": "Performance Modeling research papers in the Naser Ezzati-Jivan publication catalog.",
  "introduction": "This topic page groups Naser Ezzati-Jivan research papers related to performance modeling. Each linked record provides the paper's problem, method, findings, limitations, keywords, and authoritative source links.",
  "aliases": [
    "performance modeling"
  ],
  "search_terms": [
    "Performance Modeling"
  ],
  "related_topics": [],
  "canonical_url": "https://threadslab.org/research-publications/topics/performance-modeling.html",
  "paper_count": 3,
  "papers": [
    {
      "paper_id": "energy-consumption-analysis-large-language-models-cpu-gpu",
      "title": "Energy Consumption Analysis of Large Language Models Across CPU and GPU Using Diverse Metric Types",
      "year": 2025,
      "authors": [
        "Tong Zhang",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/energy-consumption-analysis-large-language-models-cpu-gpu/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON66301.2025.00056",
      "core_contribution": "The paper models LLM inference energy separately on CPU and GPU using hardware counters, device metrics, and task/model features, then compares classical and neural regressors across language tasks.",
      "tags": [
        "energy-efficiency",
        "llm-efficiency",
        "performance-modeling",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "LLM energy consumption",
        "CPU energy",
        "GPU energy",
        "green AI",
        "static metrics",
        "dynamic metrics",
        "NLP workloads",
        "energy prediction",
        "CASCON 2025"
      ]
    },
    {
      "paper_id": "assessing-predictive-models-energy-consumption-software",
      "title": "Assessing Predictive Models for Energy Consumption Across Varied Software Environments",
      "year": 2024,
      "authors": [
        "Tong Zhang",
        "Sarwat Islam Dipanzan",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/assessing-predictive-models-energy-consumption-software/",
      "canonical_source_url": "https://doi.org/10.1109/BigData62323.2024.10825500",
      "core_contribution": "The paper evaluates whether software-energy predictors transfer across applications when they use hardware-performance and operating-system event representations rather than application-specific measurements alone.",
      "tags": [
        "energy-efficiency",
        "performance-modeling",
        "predictive-monitoring",
        "machine-learning"
      ],
      "keywords": [
        "software energy consumption",
        "predictive models",
        "energy efficiency",
        "software environments",
        "IEEE Big Data 2024"
      ]
    },
    {
      "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"
      ]
    }
  ]
}