{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "predictive-monitoring",
  "label": "Predictive Monitoring",
  "title": "Predictive Monitoring Research",
  "description": "Predictive Monitoring research papers in the Naser Ezzati-Jivan publication catalog.",
  "introduction": "This topic page groups Naser Ezzati-Jivan research papers related to predictive monitoring. Each linked record provides the paper's problem, method, findings, limitations, keywords, and authoritative source links.",
  "aliases": [
    "predictive monitoring"
  ],
  "search_terms": [
    "Predictive Monitoring"
  ],
  "related_topics": [],
  "canonical_url": "https://threadslab.org/research-publications/topics/predictive-monitoring.html",
  "paper_count": 4,
  "papers": [
    {
      "paper_id": "gnn-link-prediction-microservices",
      "title": "Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures",
      "year": 2025,
      "authors": [
        "Ghazal Khodabandeh",
        "Alireza Ezaz",
        "Majid Babaei",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/gnn-link-prediction-microservices/",
      "canonical_source_url": "https://doi.org/10.1145/3676151.3719362",
      "core_contribution": "The paper applies graph attention networks to predict future interactions in microservice call graphs, supporting proactive monitoring.",
      "tags": [
        "microservices",
        "graph-neural-networks",
        "performance-engineering",
        "predictive-monitoring"
      ],
      "keywords": [
        "microservice call graphs",
        "link prediction",
        "graph attention networks",
        "temporal segmentation",
        "negative sampling",
        "adaptive monitoring"
      ]
    },
    {
      "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": "memadapt-adaptive-monitoring-memory-usage",
      "title": "MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data",
      "year": 2024,
      "authors": [
        "Pranjal Chakraborty",
        "Majid Babaei",
        "Leila Tahmooresnejad",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/memadapt-adaptive-monitoring-memory-usage/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON62161.2024.10838037",
      "core_contribution": "MemAdapt forecasts memory behavior under irregular sampling and uses the forecast to choose an adaptive monitoring rate that balances estimation quality with collection overhead.",
      "tags": [
        "resource-analysis",
        "predictive-monitoring",
        "performance-analysis",
        "machine-learning"
      ],
      "keywords": [
        "memory monitoring",
        "irregular sampling",
        "adaptive monitoring",
        "time series",
        "memory usage",
        "MemAdapt"
      ]
    },
    {
      "paper_id": "altoom-out-of-memory-root-cause",
      "title": "AltOOM: A Data-driven Out of Memory Root Cause Identification Strategy",
      "year": 2023,
      "authors": [
        "Pranjal Chakraborty",
        "Naser Ezzati-Jivan",
        "Vahid Azhari",
        "François Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/altoom-out-of-memory-root-cause/",
      "canonical_source_url": "https://doi.org/10.1109/bigdata59044.2023.10386937",
      "core_contribution": "AltOOM combines early memory-pressure forecasting with selective process-level profiling to identify the process most responsible for an impending out-of-memory event.",
      "tags": [
        "resource-analysis",
        "root-cause-analysis",
        "system-tracing",
        "predictive-monitoring"
      ],
      "keywords": [
        "out-of-memory",
        "OOM diagnosis",
        "data-driven RCA",
        "resource analysis",
        "memory pressure forecasting",
        "process-level profiling",
        "Perf",
        "sar"
      ]
    }
  ]
}