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  "paper_id": "memadapt-adaptive-monitoring-memory-usage",
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  "title": "MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data",
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  "authors": [
    "Pranjal Chakraborty",
    "Majid Babaei",
    "Leila Tahmooresnejad",
    "Naser Ezzati-Jivan"
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    "year": 2024,
    "venue": "2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON)",
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    "publication_date": "2024-11-11",
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    "print_date": "2024-11-11",
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    "pages": "1-6",
    "article_number": null,
    "publisher": "IEEE",
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    "doi": "10.1109/CASCON62161.2024.10838037"
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  "abstract": "The available author synopsis describes an adaptive memory-monitoring approach built around irregularly sampled data and forecasting; the full algorithm and evaluation were not captured.",
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  "description": "The available author synopsis describes an adaptive memory-monitoring approach built around irregularly sampled data and forecasting; the full algorithm and evaluation were not captured.",
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  "summary": {
    "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.",
    "problem": "Memory monitors often produce irregular observations, whereas many forecasting methods assume regular time steps. Fixed-rate sampling can oversample stable periods, undersample rapid changes, and waste monitoring resources.",
    "method": "The study derives features from sar, including %memusg, %swpused, %hugused, %user, and the inverse of %vmeff, and uses approximately 85,000 timestamps from one day. It evaluates irregular subsampling with a 900-second horizon, lookback values n=3 through 7, adaptive rates from 0.5 to 30 seconds, thresholds m=0.8 and 0.5, and theta=0.5. ODE-RNN, RNN, Kalman Filter, KF-LSTM, and related models are evaluated with an 85/15 split under sysbench/stress workloads and MSE/error metrics.",
    "findings": "ODE-RNN is the strongest model in the reported comparisons. The paper reports 78.5% of sampling-rate predictions within 5% of the ideal rate and approximately 82.5% and 83.4% within 10% and 15%, respectively. At n=7, the reported MSE comparison is 1.768 for ODE-RNN versus 1.997 for a comparison configuration. Longer history improves prediction in the evaluated traces, supporting adaptive rather than fixed-rate monitoring.",
    "limitations": "The data are derived from one day and selected workloads, so they do not establish behavior under long-running production drift, abrupt pressure events, multiple applications, or heterogeneous machines. The operational overhead and control-loop stability of deploying the adaptive policy are not fully established by the offline evaluation.",
    "future_work": "Evaluate months-long and multi-host traces, abrupt memory-pressure and swap storms, online retraining, sampling-policy stability, and end-to-end CPU/storage/network overhead in production-like monitoring agents."
  },
  "tags": [
    "resource-analysis",
    "predictive-monitoring",
    "performance-analysis",
    "machine-learning"
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  "keywords": [
    "memory monitoring",
    "irregular sampling",
    "adaptive monitoring",
    "time series",
    "memory usage",
    "MemAdapt"
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  "citation_guidance": {
    "when_to_cite": "Cite this paper when adapting memory-monitoring frequency from irregular observations and usage forecasts.",
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