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  "title": "Efficient Heap Monitoring Tool for Memory Leak Detection and Root-cause Analysis",
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  "authors": [
    "Vahid Azhari",
    "Simar Bhamra",
    "Naser Ezzati-Jivan",
    "Francois Tetreault"
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    "year": 2021,
    "venue": "2021 IEEE International Conference on Big Data (BigData)",
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    "publication_date": "2021-12-15",
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    "print_date": "2021-12-15",
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    "issue": null,
    "pages": "3020-3030",
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  "description": "The paper presents a low-intrusion heap monitor that records allocation call stacks and uses persistent heap-growth filtering plus trace correlation to identify likely memory-leak roots.",
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  "summary": {
    "core_contribution": "The paper presents a low-intrusion heap monitor that records allocation call stacks and uses persistent heap-growth filtering plus trace correlation to identify likely memory-leak roots.",
    "problem": "Long-lived C/C++ services can accumulate leaked allocations gradually, while repeated heap snapshots and tools such as Valgrind, ASAN, or LSAN can impose high overhead or require a special build and end-of-run analysis. Developers also need a causal view from leaked bytes to allocation sites.",
    "method": "An LD_PRELOAD library intercepts malloc, calloc, realloc, and free. A single heap-walker thread periodically gathers lightweight statistics; allocation headers retain call-stack information, and a shared-memory command-line interface controls the monitor. The Minimum Envelope filter removes transient jumps and dips to isolate persistent upward active-heap trends in bytes or block counts. JSON Google Trace Event output is imported into Trace Compass and correlated with LTTng kernel events; Sankey and call-graph views aggregate allocation stacks, while age-based and minimum-envelope views expose persistent allocations.",
    "findings": "The paper reports 5–12% CPU overhead and less than 10% heap-memory impact in its stated implementation. Synthetic leak probabilities of 0.1, 0.01, 0.001, and 0.0001 over 60-minute runs converge within 10% of the nominal rate, with approximately 91% precision in the reported evaluation. Industrial nested-call, binary-search-tree, and linked-list cases illustrate root-cause localization; at a 0.001 leak probability, Valgrind leak-check=full is reported at roughly six times native CPU cost.",
    "limitations": "The quantitative evidence combines synthetic leak injection with a limited industrial example, and threshold selection affects persistence detection and false positives. The paper calls for 90–100 additional runs to strengthen precision estimates and notes that source-file and line rendering in the call graph needs improvement.",
    "future_work": "Run larger production-like studies, quantify recall and time-to-detection under allocator and concurrency variation, improve source mapping and threshold calibration, and evaluate false-positive control when legitimate caches or workload phases grow the heap persistently."
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  "tags": [
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    "anomaly-detection",
    "performance-analysis",
    "system-tracing"
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    "heap monitoring",
    "memory leaks",
    "root-cause analysis",
    "memory management",
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    "when_to_cite": "Cite this paper when your work monitors heap growth with low-intrusion allocation interposition and call-stack root-cause views.",
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