{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "resource-analysis",
  "label": "Resource Analysis",
  "title": "Resource Analysis Research",
  "description": "Resource Analysis research papers in the Naser Ezzati-Jivan publication catalog.",
  "introduction": "This topic page groups Naser Ezzati-Jivan research papers related to resource analysis. Each linked record provides the paper's problem, method, findings, limitations, keywords, and authoritative source links.",
  "aliases": [
    "resource analysis"
  ],
  "search_terms": [
    "Resource Analysis"
  ],
  "related_topics": [],
  "canonical_url": "https://threadslab.org/research-publications/topics/resource-analysis.html",
  "paper_count": 17,
  "papers": [
    {
      "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"
      ]
    },
    {
      "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"
      ]
    },
    {
      "paper_id": "container-workload-characterization-host-tracing",
      "title": "Container Workload Characterization Through Host System Tracing",
      "year": 2021,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Seyed Vahid Azhari"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/container-workload-characterization-host-tracing/",
      "canonical_source_url": "https://doi.org/10.1109/ic2e52221.2021.00015",
      "core_contribution": "The paper characterizes container workloads from host-level LTTng traces by selecting important threads and clustering execution-state behavior without instrumenting the containers internally.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "resource-analysis"
      ],
      "keywords": [
        "container workloads",
        "host tracing",
        "LTTng",
        "pid_ns",
        "tid",
        "Trace Compass",
        "PageRank",
        "K-Means",
        "Docker",
        "Ubuntu 20.04.1",
        "Linux 5.8.0",
        "silhouette score",
        "tracing overhead"
      ]
    },
    {
      "paper_id": "efficient-heap-monitoring-memory-leak-root-cause",
      "title": "Efficient Heap Monitoring Tool for Memory Leak Detection and Root-cause Analysis",
      "year": 2021,
      "authors": [
        "Vahid Azhari",
        "Simar Bhamra",
        "Naser Ezzati-Jivan",
        "Francois Tetreault"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-heap-monitoring-memory-leak-root-cause/",
      "canonical_source_url": "https://doi.org/10.1109/BigData52589.2021.9671473",
      "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.",
      "tags": [
        "resource-analysis",
        "anomaly-detection",
        "performance-analysis",
        "system-tracing"
      ],
      "keywords": [
        "heap monitoring",
        "memory leaks",
        "root-cause analysis",
        "memory management",
        "BigData 2021"
      ]
    },
    {
      "paper_id": "system-execution-path-profiling-hardware-counters",
      "title": "System Execution Path Profiling Using Hardware Performance Counters",
      "year": 2021,
      "authors": [
        "Francis Giraldeau",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/system-execution-path-profiling-hardware-counters/",
      "canonical_source_url": "https://doi.org/10.1109/SYSCON48628.2021.9447121",
      "core_contribution": "The work bridges kernel-level hardware-counter sampling and user-space interpreter state so execution-path profiling can attribute performance evidence to interpreted code.",
      "tags": [
        "performance-analysis",
        "resource-analysis",
        "multicore-systems",
        "system-tracing"
      ],
      "keywords": [
        "execution paths",
        "hardware performance counters",
        "critical-path profiling",
        "CPython",
        "PyPMU",
        "Linux perf",
        "perf NMI",
        "LTTng-UST",
        "libunwind",
        "sampling",
        "SysCon 2021"
      ]
    },
    {
      "paper_id": "anomaly-detection-ddos-attacks-cloud-environment",
      "title": "The Use of Anomaly Detection for the Detection of Different Types of DDoS Attacks in Cloud Environment",
      "year": 2021,
      "authors": [
        "Hossein Abbasi",
        "Naser Ezzati-Jivan",
        "Martine Bellaiche",
        "Chamseddine Talhi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/anomaly-detection-ddos-attacks-cloud-environment/",
      "canonical_source_url": "https://doi.org/10.1007/s41635-021-00119-z",
      "core_contribution": "The paper proposes a cloud-side anomaly detector that combines traffic, virtual-machine resource, and kernel-level indicators to identify several DDoS classes through change-point evidence.",
      "tags": [
        "anomaly-detection",
        "machine-learning",
        "resource-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "DDoS attacks",
        "cloud environment",
        "CUSUM",
        "bandwidth exhaustion",
        "application exhaustion",
        "connection exhaustion",
        "anomaly detection"
      ]
    },
    {
      "paper_id": "cloud-platform-performance-multilevel-tracing",
      "title": "Cloud Platform Performance Evaluation Using Multi-level Execution Tracing",
      "year": 2020,
      "authors": [
        "Yves J. Bationo",
        "Naser Ezzati-Jivan",
        "Evan Galea",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/cloud-platform-performance-multilevel-tracing/",
      "canonical_source_url": "https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00063",
      "core_contribution": "The paper correlates LTTng traces across OpenStack services, QEMU/KVM, network components, and host kernels to diagnose cloud-platform performance problems.",
      "tags": [
        "system-tracing",
        "performance-analysis",
        "performance-engineering",
        "resource-analysis"
      ],
      "keywords": [
        "cloud platforms",
        "multi-level execution tracing",
        "LTTng",
        "LTTng-UST",
        "OpenStack Nova",
        "Neutron",
        "QEMU",
        "KVM",
        "Open vSwitch",
        "Trace Compass",
        "live VM migration",
        "preemption",
        "VM interference"
      ]
    },
    {
      "paper_id": "machine-learning-based-edos-attack-detection-technique-using-execution-trace-analysis",
      "title": "Machine Learning-Based EDoS Attack Detection Technique Using Execution Trace Analysis",
      "year": 2019,
      "authors": [
        "Hossein Abbasi",
        "Naser Ezzati-Jivan",
        "Martine Bellaiche",
        "Chamseddine Talhi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/machine-learning-based-edos-attack-detection-technique-using-execution-trace-analysis/",
      "canonical_source_url": "https://doi.org/10.1007/s41635-018-0061-2",
      "core_contribution": "The paper combines execution-trace and virtual-machine metrics with machine learning to detect EDoS behavior and restrict resource expansion to apparently normal VMs.",
      "tags": [
        "anomaly-detection",
        "kernel-tracing",
        "machine-learning",
        "performance-analysis",
        "resource-analysis",
        "system-tracing"
      ],
      "keywords": [
        "Economic Denial of Sustainability",
        "EDoS",
        "DDoS",
        "cloud computing",
        "execution trace analysis",
        "LTTng",
        "QEMU",
        "KVM",
        "Apache",
        "MySQL",
        "HTTPFlooder",
        "LoadRunner",
        "WEKA",
        "multilayer perceptron",
        "SVM",
        "Snort"
      ]
    },
    {
      "paper_id": "dynamic-trace-sampling-memory-usage",
      "title": "Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications",
      "year": 2017,
      "authors": [
        "Houssem Daoud",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/dynamic-trace-sampling-memory-usage/",
      "canonical_source_url": "https://doi.org/10.1109/HPEC.2017.8091061",
      "core_contribution": "The paper introduces a kernel-resident dynamic sampler that tracks virtual and physical memory usage while reducing the event volume generated by high-frequency memory activity.",
      "tags": [
        "kernel-tracing",
        "resource-analysis",
        "performance-analysis",
        "trace-reduction"
      ],
      "keywords": [
        "memory usage",
        "dynamic sampling",
        "enterprise applications",
        "trace overhead",
        "LTTng",
        "kernel module",
        "Kprobes",
        "kmem_mm_page_alloc",
        "kmem_mm_page_free"
      ]
    },
    {
      "paper_id": "enhanced-state-history-tree-esht-a-stateful-data-structure-for-analysis-of-highly-parallel-syste",
      "title": "Enhanced State History Tree (eSHT): A Stateful Data Structure for Analysis of Highly Parallel System Traces",
      "year": 2016,
      "authors": [
        "Loic Prieur-Drevon",
        "Raphael Beamonte",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/enhanced-state-history-tree-esht-a-stateful-data-structure-for-analysis-of-highly-parallel-syste/",
      "canonical_source_url": "https://doi.org/10.1109/BigDataCongress.2016.19",
      "core_contribution": "eSHT redesigns the State History Tree to store and query highly parallel trace intervals more compactly and with shallower trees.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-optimization",
        "multicore-systems",
        "resource-analysis"
      ],
      "keywords": [
        "Enhanced State History Tree",
        "eSHT",
        "State History Tree",
        "interval data",
        "external-memory indexing",
        "LTTng 2.7.0",
        "Trace Compass",
        "Linux kernel 4.3.0",
        "parallel traces",
        "node fill",
        "tree depth",
        "query optimization"
      ]
    },
    {
      "paper_id": "multilayer-virtualized-systems-kernel-tracing",
      "title": "Multilayer Virtualized Systems Analysis with Kernel Tracing",
      "year": 2016,
      "authors": [
        "Cedric Biancheri",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multilayer-virtualized-systems-kernel-tracing/",
      "canonical_source_url": "https://doi.org/10.1109/W-FiCloud.2016.18",
      "core_contribution": "The paper fuses synchronized host and virtual-machine kernel traces into a Fused Virtual Machine view that exposes how VM threads, virtual CPUs, hypervisor transitions, and physical CPUs interact.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "resource-analysis"
      ],
      "keywords": [
        "virtualized systems",
        "kernel tracing",
        "multilayer analysis",
        "virtualization overhead",
        "LTTng",
        "KVM",
        "Trace Compass",
        "Fused Virtual Machine view",
        "State History Tree",
        "vmentry",
        "vmexit"
      ]
    },
    {
      "paper_id": "cube-data-model-for-multilevel-statistics-computation-of-live-execution-traces",
      "title": "Cube data model for multilevel statistics computation of live execution traces",
      "year": 2015,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/cube-data-model-for-multilevel-statistics-computation-of-live-execution-traces/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.3272",
      "core_contribution": "The paper proposes a multilevel data-cube model for computing statistics over live execution traces across several time scales.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-analysis",
        "resource-analysis",
        "performance-engineering"
      ],
      "keywords": [
        "data cube",
        "stream cube",
        "live trace statistics",
        "LTTng",
        "State History Tree",
        "tilted time frame",
        "multilevel analysis",
        "roll-up",
        "drill-down",
        "range query",
        "sliding window",
        "cuboid materialization"
      ]
    },
    {
      "paper_id": "a-framework-to-compute-statistics-of-system-parameters-from-very-large-trace-files",
      "title": "A framework to compute statistics of system parameters from very large trace files",
      "year": 2013,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-framework-to-compute-statistics-of-system-parameters-from-very-large-trace-files/",
      "canonical_source_url": "https://doi.org/10.1145/2433140.2433151",
      "core_contribution": "The framework computes system-parameter statistics for arbitrary intervals and resource hierarchies from very large traces using a disk-resident history structure and controlled granularity.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-analysis",
        "resource-analysis",
        "performance-engineering"
      ],
      "keywords": [
        "trace statistics",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "disk-resident interval tree",
        "history tree",
        "granularity degree",
        "linear interpolation",
        "hierarchical query",
        "large-scale tracing",
        "online analysis",
        "CPU usage",
        "I/O throughput"
      ]
    },
    {
      "paper_id": "state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data",
      "title": "State History Tree: An Incremental Disk-Based Data Structure for Very Large Interval Data",
      "year": 2013,
      "authors": [
        "Alexandre Montplaisir-Goncalves",
        "Naser Ezzati-Jivan",
        "Florian Wininger",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/state-history-tree-an-incremental-disk-based-data-structure-for-very-large-interval-data/",
      "canonical_source_url": "https://doi.org/10.1109/SocialCom.2013.107",
      "core_contribution": "The paper presents a disk-oriented State History Tree that stores very large, time-ordered trace intervals without repeated node rebalancing.",
      "tags": [
        "kernel-tracing",
        "trace-analysis",
        "performance-engineering",
        "resource-analysis"
      ],
      "keywords": [
        "State History Tree",
        "disk-based interval data",
        "streaming interval data",
        "time-ordered intervals",
        "LTTng",
        "Eclipse Tracing and Monitoring Framework",
        "Linux Tools Project",
        "Java",
        "R-tree",
        "PostgreSQL/PostGIS",
        "full query",
        "ad-hoc query"
      ]
    },
    {
      "paper_id": "high-performance-internet-connection-filtering-in-kernel",
      "title": "High Performance Internet Connection Filtering through an In-Kernel Architecture",
      "year": 2011,
      "authors": [
        "Naser Ezzati Jivan",
        "Alireza Shameli Sendi",
        "Naser Nematbakhsh",
        "Michel Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/high-performance-internet-connection-filtering-in-kernel/",
      "canonical_source_url": "https://publications.polymtl.ca/16917/",
      "core_contribution": "The paper moves part of an application proxy into the kernel to reduce context switches and unnecessary data copies during connection filtering.",
      "tags": [
        "performance-engineering",
        "performance-optimization",
        "resource-analysis"
      ],
      "keywords": [
        "in-kernel proxy",
        "connection filtering",
        "Fire Bench",
        "SPECWeb2009",
        "context switches",
        "data copies",
        "HTTP proxy",
        "FTP",
        "TELNET",
        "kernel architecture"
      ]
    },
    {
      "paper_id": "a-reliable-and-efficient-time-synchronization-protocol-for-heterogeneous-wireless-sensor-network",
      "title": "A Reliable and Efficient Time Synchronization Protocol for Heterogeneous Wireless Sensor Network",
      "year": 2010,
      "authors": [
        "Masoume Jabbarifar",
        "Alireza Shameli-Sendi",
        "Alireza Sadighian",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-reliable-and-efficient-time-synchronization-protocol-for-heterogeneous-wireless-sensor-network/",
      "canonical_source_url": "https://doi.org/10.4236/wsn.2010.212109",
      "core_contribution": "The paper proposes L-SYNCng, a time-synchronization protocol for heterogeneous wireless sensor networks that combines topology-aware clustering with convex-hull clock estimation.",
      "tags": [
        "performance-analysis",
        "resource-analysis",
        "observability"
      ],
      "keywords": [
        "wireless sensor networks",
        "time synchronization",
        "L-SYNCng",
        "convex hull",
        "clock drift",
        "NS-2.31",
        "heterogeneous topology",
        "clock offset",
        "clock skew"
      ]
    }
  ]
}