{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "performance-analysis",
  "label": "Performance Analysis",
  "title": "Software Performance Analysis Research",
  "description": "Research papers on software performance analysis, bottleneck localization, latency, overhead, and execution behavior.",
  "introduction": "This topic is the broad performance-analysis entry point for the catalog. It connects work on bottleneck localization, latency anomalies, resource contention, execution paths, tracing overhead, performance modeling, and diagnosis across multicore, cloud, web, and distributed systems.",
  "aliases": [
    "software performance analysis",
    "performance diagnosis",
    "performance debugging"
  ],
  "search_terms": [
    "software performance",
    "performance bottlenecks",
    "latency analysis",
    "performance debugging",
    "execution profiling"
  ],
  "related_topics": [
    "performance-engineering",
    "latency-analysis",
    "root-cause-analysis",
    "kernel-tracing",
    "observability"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/performance-analysis.html",
  "paper_count": 50,
  "papers": [
    {
      "paper_id": "b-perf-black-box-performance-antipatterns",
      "title": "B-Perf: Black-box Performance Antipattern Detection Using System-level Execution Tracing",
      "year": 2026,
      "authors": [
        "Morteza Noferesti",
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/b-perf-black-box-performance-antipatterns/",
      "canonical_source_url": "https://doi.org/10.1145/3777884.3797014",
      "core_contribution": "B-Perf reconstructs execution, memory, and messaging behavior from Linux kernel-level traces and infers performance-antipattern indicators without requiring application source access or intrusive instrumentation.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "anomaly-detection"
      ],
      "keywords": [
        "performance antipatterns",
        "black-box detection",
        "system-level execution traces",
        "LTTng",
        "Trace Compass",
        "critical paths",
        "resource contention",
        "execution serialization",
        "allocation churn",
        "small-message overhead"
      ]
    },
    {
      "paper_id": "observability-runtime-governance-agentic-ai-systems",
      "title": "Observability and Runtime Governance for Agentic AI Systems",
      "year": 2026,
      "authors": [
        "Naser Ezzati-Jivan",
        "Maryam Ekhlasi"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/observability-runtime-governance-agentic-ai-systems/",
      "canonical_source_url": "https://doi.org/10.1145/3803437.3804904",
      "core_contribution": "The tutorial presents an end-to-end AgentOps workflow that connects task intent and model decisions to tool calls, memory access, inter-agent communication, external side effects, and runtime-governance decisions.",
      "tags": [
        "observability",
        "llm-assisted-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "agentic AI",
        "AgentOps",
        "runtime governance",
        "AI observability",
        "software agents",
        "agent tracing",
        "silent failures",
        "goal drift",
        "tool-use failures",
        "cross-layer evidence",
        "policy checks",
        "human escalation",
        "FSE 2026"
      ]
    },
    {
      "paper_id": "rethinking-performance-debugging-from-optimization-to-collaborative-reasoning",
      "title": "Rethinking Performance Debugging: From Optimization to Collaborative Reasoning",
      "year": 2026,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/rethinking-performance-debugging-from-optimization-to-collaborative-reasoning/",
      "canonical_source_url": "https://doi.org/10.1145/3803437.3805578",
      "core_contribution": "The paper reframes performance debugging as collaborative reasoning over multiple evidence-grounded hypotheses rather than optimization for one supposedly best explanation.",
      "tags": [
        "performance-analysis",
        "microservices",
        "llm-assisted-analysis",
        "root-cause-analysis",
        "observability"
      ],
      "keywords": [
        "performance debugging",
        "collaborative reasoning",
        "AgentDebug",
        "Reasoning Surface",
        "hypothesis generation",
        "hypothesis critique",
        "hypothesis curation",
        "Gemini 2.5 Flash",
        "CART",
        "DeLag",
        "Train-Ticket",
        "E-Shopper",
        "fault injection",
        "RPC latency",
        "Pareto-optimal hypotheses"
      ]
    },
    {
      "paper_id": "developing-a-taxonomy-for-advanced-log-parsing-techniques",
      "title": "Developing a Taxonomy for Advanced Log Parsing Techniques",
      "year": 2025,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait Mohamed",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/developing-a-taxonomy-for-advanced-log-parsing-techniques/",
      "canonical_source_url": "https://doi.org/10.1109/ICPC66645.2025.00061",
      "core_contribution": "The paper introduces a taxonomy of log-event characteristics that explains why different log parsers fail across systems and parser families.",
      "tags": [
        "observability",
        "trace-analysis",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "log parsing",
        "log event characteristics",
        "LEC taxonomy",
        "LogHub",
        "Drain",
        "IPLoM",
        "AEL",
        "Spell",
        "LenMa",
        "LogMine",
        "SHISO",
        "ULP",
        "open coding",
        "regex",
        "NER",
        "chi-square",
        "parser errors",
        "token structure"
      ]
    },
    {
      "paper_id": "efficient-communication-performance-antipattern-detection-microservices",
      "title": "Efficient Detection of Communication-related Performance Anti-patterns in Microservices",
      "year": 2025,
      "authors": [
        "Masoumeh Nourollahi",
        "Naser Ezzati-Jivan",
        "Adel Belkheiri",
        "Michel Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-communication-performance-antipattern-detection-microservices/",
      "canonical_source_url": "https://doi.org/10.22541/au.175533132.24109345/v1",
      "core_contribution": "The preprint combines communication-related kernel/system-call events with distributed traces to detect communication performance anti-patterns in microservices.",
      "tags": [
        "microservices",
        "system-tracing",
        "anomaly-detection",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "communication anti-patterns",
        "DeathStarBench",
        "LTTng",
        "Trace Compass",
        "Jaeger",
        "system calls",
        "distributed traces",
        "Blob",
        "Empty Semi Trucks",
        "supervised learning",
        "semi-supervised learning"
      ]
    },
    {
      "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": "execution-trace-reconstruction-using-diffusion-based-generative-models",
      "title": "Execution Trace Reconstruction Using Diffusion-Based Generative Models",
      "year": 2025,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/execution-trace-reconstruction-using-diffusion-based-generative-models/",
      "canonical_source_url": "https://doi.org/10.1109/ICSE55347.2025.00063",
      "core_contribution": "The paper reconstructs missing system-call events in execution traces with diffusion and structured state-space generative models.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-analysis",
        "trace-reduction",
        "performance-analysis"
      ],
      "keywords": [
        "execution trace reconstruction",
        "trace imputation",
        "diffusion models",
        "DiffWave",
        "SSSDS4",
        "SSSDSA",
        "CSDIS4",
        "structured state-space",
        "system-call sequences",
        "LTTng",
        "Phoronix Test Suite",
        "ROUGE-L",
        "perfect rate",
        "missing events",
        "ring-buffer overflow"
      ]
    },
    {
      "paper_id": "analyzing-performance-variability-in-alibaba-s-microservice-architecture-a-critical-path-based-p",
      "title": "Analyzing Performance Variability in Alibaba's Microservice Architecture: A Critical-Path-Based Perspective",
      "year": 2024,
      "authors": [
        "Alireza Ezaz",
        "Ghazal Khodabandeh",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/analyzing-performance-variability-in-alibaba-s-microservice-architecture-a-critical-path-based-p/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651845",
      "core_contribution": "The paper identifies response-time variability in Alibaba microservice traces through critical-path extraction and variability analysis of service interactions.",
      "tags": [
        "microservices",
        "performance-analysis",
        "latency-analysis",
        "observability",
        "performance-engineering"
      ],
      "keywords": [
        "Alibaba microservice architecture",
        "critical path",
        "distributed traces",
        "response-time variability",
        "critical interactions",
        "microservice performance",
        "adaptive tracing",
        "cluster-trace-microservices-v2022",
        "mean response time",
        "standard deviation"
      ]
    },
    {
      "paper_id": "context-aware-root-cause-localization-in-distributed-traces-using-social-network-analysis-work-i",
      "title": "Context-aware Root Cause Localization in Distributed Traces Using Social Network Analysis (Work In Progress paper)",
      "year": 2024,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj",
        "James Miller"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/context-aware-root-cause-localization-in-distributed-traces-using-social-network-analysis-work-i/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651426",
      "core_contribution": "The work-in-progress paper combines service-call graph context, social-network analysis, and spectrum-based fault localization to rank distributed-trace root causes.",
      "tags": [
        "root-cause-analysis",
        "microservices",
        "social-network-analysis",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "context-aware RCA",
        "service-call graph",
        "distributed traces",
        "service communities",
        "Louvain",
        "PageRank",
        "Jaccard distance",
        "Ochiai",
        "CPU stress",
        "network delay",
        "network loss",
        "AIOps Challenge 2020"
      ]
    },
    {
      "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": "network-analysis-microservices-alibaba",
      "title": "Network Analysis of Microservices: A Case Study on Alibaba Production Clusters",
      "year": 2024,
      "authors": [
        "Ghazal Khodabandeh",
        "Alireza Ezaz",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/network-analysis-microservices-alibaba/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651842",
      "core_contribution": "The paper applies graph community detection and service-graph clustering to expose recurring microservice communication structures in an Alibaba production-cluster snapshot.",
      "tags": [
        "microservices",
        "performance-analysis",
        "observability",
        "social-network-analysis"
      ],
      "keywords": [
        "microservice networks",
        "Alibaba production clusters",
        "service call graphs",
        "community detection",
        "Louvain",
        "Infomap",
        "Label Propagation",
        "graph clustering",
        "K-Means",
        "silhouette score",
        "modularity"
      ]
    },
    {
      "paper_id": "resource-life-cycle-aware-noise-detection-kernel-events",
      "title": "Resource Life-Cycle Aware Noise Detection via Kernel Event Monitoring",
      "year": 2024,
      "authors": [
        "Morteza Noferesti",
        "Ben Grandy",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/resource-life-cycle-aware-noise-detection-kernel-events/",
      "canonical_source_url": "https://doi.org/10.1109/CASCON62161.2024.10838205",
      "core_contribution": "The paper detects and localizes performance noise by aligning passive kernel-event evidence with the life cycle of CPU, disk, and network requests rather than treating resource utilization as an undifferentiated aggregate.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "kernel event monitoring",
        "resource life cycle",
        "noise detection",
        "CPU requests",
        "disk requests",
        "network requests"
      ]
    },
    {
      "paper_id": "emd-scs-malware-detection-sonification-system-calls",
      "title": "EMD-SCS: A Dynamic Behavioral Approach for Early Malware Detection with Sonification of System Call Sequences",
      "year": 2023,
      "authors": [
        "Raghav Bhardwaj",
        "Morteza Noferesti",
        "Madeline Janecek",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/emd-scs-malware-detection-sonification-system-calls/",
      "canonical_source_url": "https://doi.org/10.1109/TRUSTCOM60117.2023.00235",
      "core_contribution": "EMD-SCS combines sequence prediction of system calls with sonification so that partial execution prefixes can support early malware detection and an interpretable auditory alert.",
      "tags": [
        "system-tracing",
        "anomaly-detection",
        "machine-learning",
        "performance-analysis"
      ],
      "keywords": [
        "malware detection",
        "system-call sequences",
        "sonification",
        "Hamming distance",
        "detection rate",
        "false-positive rate",
        "EMD-SCS"
      ]
    },
    {
      "paper_id": "identification-and-classification-of-jmh-microbenchmark-states-using-time-series-analysis",
      "title": "Identification and Classification of JMH Microbenchmark States using Time Series Analysis",
      "year": 2023,
      "authors": [
        "Tom Wallace",
        "Beatrice M. Ombuki-Berman",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/identification-and-classification-of-jmh-microbenchmark-states-using-time-series-analysis/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584694",
      "core_contribution": "The paper uses time-series analysis to identify and classify warmup, steady-state, and anomalous states in Java JMH microbenchmarks.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "anomaly-detection",
        "machine-learning"
      ],
      "keywords": [
        "Java Microbenchmark Harness",
        "JMH",
        "benchmark states",
        "steady state",
        "warmup",
        "Matrix Profile",
        "motifs",
        "discords",
        "PELT",
        "change points",
        "Stumpy",
        "Ruptures",
        "time-series analysis"
      ]
    },
    {
      "paper_id": "multi-level-adaptive-execution-tracing-performance-analysis",
      "title": "Multi-level Adaptive Execution Tracing for Efficient Performance Analysis",
      "year": 2023,
      "authors": [
        "Mohammed Adib Khan",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multi-level-adaptive-execution-tracing-performance-analysis/",
      "canonical_source_url": "https://doi.org/10.1109/SERA57763.2023.10197790",
      "core_contribution": "The paper presents a multi-level adaptive tracing workflow that uses lightweight stress detection and performance evidence to change the application/kernel instrumentation scope during an investigation.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "adaptive-scheduling"
      ],
      "keywords": [
        "adaptive execution tracing",
        "time-series trends",
        "multi-level tracing",
        "kernel tracing",
        "performance analysis",
        "SERA 2023"
      ]
    },
    {
      "paper_id": "pasd-statistical-debugging-kernel-events",
      "title": "PASD: A Performance Analysis Approach Through the Statistical Debugging of Kernel Events",
      "year": 2023,
      "authors": [
        "Mohammed Adib Khan",
        "Morteza Noferesti",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/pasd-statistical-debugging-kernel-events/",
      "canonical_source_url": "https://doi.org/10.1109/scam59687.2023.00025",
      "core_contribution": "PASD uses Linux kernel-event traces, Perf call stacks, and statistical debugging to rank functions associated with performance problems without application-source instrumentation.",
      "tags": [
        "kernel-tracing",
        "performance-analysis",
        "root-cause-analysis",
        "anomaly-detection"
      ],
      "keywords": [
        "statistical debugging",
        "Linux kernel tracepoints",
        "Perf",
        "perf record",
        "call stacks",
        "sched_switch",
        "Firefox",
        "Coreutils",
        "ls",
        "Bug 1637586",
        "Bug 1565019",
        "function suspect ranking"
      ]
    },
    {
      "paper_id": "software-mining-investigating-correlation-between-source-code-features-and-michrobenchmark-s-ste",
      "title": "Software Mining - Investigating Correlation between Source Code Features and Michrobenchmark's Steady State",
      "year": 2023,
      "authors": [
        "Amirmahdi Khosravi Tabrizi",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/software-mining-investigating-correlation-between-source-code-features-and-michrobenchmark-s-ste/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584695",
      "core_contribution": "The study examines whether static source-code features are associated with the steady-state behavior of Java microbenchmarks during JVM warmup.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "machine-learning"
      ],
      "keywords": [
        "Java Microbenchmark Harness",
        "JMH",
        "srcML",
        "Lizard",
        "Apriori",
        "Java microbenchmarks",
        "JVM warmup",
        "steady state",
        "source-code features",
        "BMNFC",
        "BMLOC",
        "DNNSF"
      ]
    },
    {
      "paper_id": "towards-a-classification-of-log-parsing-errors",
      "title": "Towards a Classification of Log Parsing Errors",
      "year": 2023,
      "authors": [
        "Issam Sedki",
        "Abdelwahab Hamou-Lhadj",
        "Otmane Ait Mohamed",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/towards-a-classification-of-log-parsing-errors/",
      "canonical_source_url": "https://doi.org/10.1109/ICPC58990.2023.00023",
      "core_contribution": "The paper classifies recurring log-parsing errors and connects them to token and event characteristics that can guide parser design and logging practice.",
      "tags": [
        "observability",
        "trace-analysis",
        "anomaly-detection",
        "performance-analysis"
      ],
      "keywords": [
        "log parsing",
        "parser errors",
        "Drain",
        "AEL",
        "Spell",
        "ULP",
        "LogHub",
        "tokenization",
        "logging practices",
        "dynamic tokens",
        "static tokens",
        "delimiter-aware logging",
        "parser error taxonomy"
      ]
    },
    {
      "paper_id": "uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis",
      "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
      "year": 2023,
      "authors": [
        "Madeline Janecek",
        "Sneh Patel",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis/",
      "canonical_source_url": "https://doi.org/10.1145/3578245.3584689",
      "core_contribution": "The paper uses sampled call graphs and machine learning to distinguish warmup from steady-state executions in Java microbenchmarks.",
      "tags": [
        "performance-analysis",
        "performance-engineering",
        "machine-learning",
        "trace-analysis"
      ],
      "keywords": [
        "Java",
        "JMH",
        "microbenchmarking",
        "steady-state detection",
        "warmup",
        "call-graph analysis",
        "Perf",
        "Graph2Vec",
        "change-point detection",
        "PELT",
        "Random Forest",
        "SVC",
        "flame graphs"
      ]
    },
    {
      "paper_id": "n-lane-bridge-performance-antipatterns",
      "title": "N-Lane Bridge Performance Antipattern Analysis Using System-Level Execution Tracing",
      "year": 2022,
      "authors": [
        "Riley VanDonge",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/n-lane-bridge-performance-antipatterns/",
      "canonical_source_url": "https://doi.org/10.1109/scam55253.2022.00015",
      "core_contribution": "N-Lane Bridge extends One-Lane Bridge analysis to active-resource bottlenecks and uses LTTng system-level traces to distinguish application-caused congestion from external interference.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "anomaly-detection"
      ],
      "keywords": [
        "N-Lane Bridge",
        "One-Lane Bridge",
        "performance antipatterns",
        "system-level execution tracing",
        "LTTng",
        "Trace Compass",
        "sched_switch",
        "sched_wakeup",
        "futex",
        "Firefox PDF.js",
        "InDebitO",
        "SysBench",
        "memory paging",
        "CPU preemption"
      ]
    },
    {
      "paper_id": "performance-anomaly-detection-sequence-alignment",
      "title": "Performance Anomaly Detection through Sequence Alignment of System-Level Traces",
      "year": 2022,
      "authors": [
        "Madeline Janecek",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/performance-anomaly-detection-sequence-alignment/",
      "canonical_source_url": "https://doi.org/10.1145/3524610.3527898",
      "core_contribution": "The framework combines critical-path extraction, coarse anomaly checks, OPTICS execution clustering, and MUSCLE multiple sequence alignment to localize performance differences in system-level traces.",
      "tags": [
        "system-tracing",
        "anomaly-detection",
        "performance-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "sequence alignment",
        "MUSCLE",
        "OPTICS",
        "critical path",
        "system-level traces",
        "performance anomalies",
        "LTTng",
        "Docker",
        "execution clustering",
        "trace overhead"
      ]
    },
    {
      "paper_id": "performance-evaluation-multithread-execution-paths",
      "title": "Performance Evaluation of Complex Multi-Thread Applications Through Execution Path Analysis",
      "year": 2022,
      "authors": [
        "Majid Rezazadeh",
        "Naser Ezzati-Jivan",
        "Seyed Vahid Azhari",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/performance-evaluation-multithread-execution-paths/",
      "canonical_source_url": "https://doi.org/10.1016/j.peva.2022.102289",
      "core_contribution": "The paper unifies Chromium user-space events with Linux kernel scheduling and resource events to analyze execution paths and localize sporadic latency in a complex multithreaded application.",
      "tags": [
        "performance-analysis",
        "trace-analysis",
        "multicore-systems",
        "performance-engineering"
      ],
      "keywords": [
        "multi-thread applications",
        "execution paths",
        "performance evaluation",
        "thread behavior"
      ]
    },
    {
      "paper_id": "operating-system-support-applications-performance-analysis-poster",
      "title": "Poster Paper: Operating System Support for Applications Performance Analysis",
      "year": 2022,
      "authors": [
        "Riley VanDonge",
        "Naser Ezzati-Jivan"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/operating-system-support-applications-performance-analysis-poster/",
      "canonical_source_url": "https://doi.org/10.1109/IC2E55432.2022.00039",
      "core_contribution": "This poster proposes operating-system-aware antipattern detection that uses system-level traces and scalability trends to expose resource blocking that aggregate CPU utilization can hide.",
      "tags": [
        "performance-analysis",
        "system-tracing",
        "kernel-tracing"
      ],
      "keywords": [
        "operating-system support",
        "application performance analysis",
        "performance instrumentation",
        "IC2E 2022",
        "poster paper"
      ]
    },
    {
      "paper_id": "model-based-constraints-execution-tracing",
      "title": "Automated Generation of Model-Based Constraints for Common Multi-core and Real-Time Applications Using Execution Tracing",
      "year": 2021,
      "authors": [
        "Raphael Beamonte",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/model-based-constraints-execution-tracing/",
      "canonical_source_url": "https://doi.org/10.1007/s10766-020-00689-5",
      "core_contribution": "The paper automatically builds an approximate workflow model and adaptive quantitative constraints from user-space and kernel execution traces, reducing manual model construction for real-time and multicore diagnosis.",
      "tags": [
        "system-tracing",
        "multicore-systems",
        "trace-analysis",
        "performance-analysis",
        "lttng"
      ],
      "keywords": [
        "model-based constraints",
        "execution tracing",
        "multi-core systems",
        "real-time applications",
        "constraint generation",
        "LTTng",
        "Trace Compass",
        "cyclictest",
        "JACK2",
        "wakelock"
      ]
    },
    {
      "paper_id": "combining-distributed-kernel-tracing-cloud",
      "title": "Combining Distributed and Kernel Tracing for Performance Analysis of Cloud Applications",
      "year": 2021,
      "authors": [
        "Loïc Gelle",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/combining-distributed-kernel-tracing-cloud/",
      "canonical_source_url": "https://doi.org/10.3390/electronics10212610",
      "core_contribution": "The paper combines Jaeger distributed traces with LTTng kernel and user-space events and extends critical-path analysis from threads to distributed requests so request latency can be related to low-level scheduling and resource behavior.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "observability",
        "lttng"
      ],
      "keywords": [
        "distributed tracing",
        "kernel tracing",
        "cloud applications",
        "cross-layer performance analysis",
        "LTTng",
        "Jaeger",
        "Trace Compass",
        "Babeltrace",
        "Common Trace Format",
        "HotROD",
        "Cassandra",
        "Apache ab",
        "cassandra-stress",
        "critical-path analysis",
        "snapshot tracing",
        "CPU control groups",
        "liblttng-ust",
        "request synchronization"
      ]
    },
    {
      "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": "debugging-performance-degradation-distributed-requests",
      "title": "Debugging of Performance Degradation in Distributed Requests Handling Using Multilevel Trace Analysis",
      "year": 2021,
      "authors": [
        "Naser Ezzati-Jivan",
        "Houssem Daoud",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/debugging-performance-degradation-distributed-requests/",
      "canonical_source_url": "https://doi.org/10.1155/2021/8478076",
      "core_contribution": "The paper correlates LTTng traces from user space through kernel, storage, network, and multiple hosts in a disk-backed state model, enabling top-down diagnosis of distributed request latency.",
      "tags": [
        "system-tracing",
        "microservices",
        "performance-analysis",
        "root-cause-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "distributed requests",
        "multilevel trace analysis",
        "LTTng",
        "Apache",
        "PHP",
        "MariaDB",
        "State History Tree",
        "cache hit ratio",
        "disk contention",
        "ApacheBench",
        "Trace Compass"
      ]
    },
    {
      "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": "integrated-modeling-tool-state-machine-trace",
      "title": "Integrated Modeling Tool for Indexing and Analyzing State Machine Trace",
      "year": 2021,
      "authors": [
        "Simon Delisle",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/integrated-modeling-tool-state-machine-trace/",
      "canonical_source_url": "https://doi.org/10.1109/ISNCC52172.2021.9615814",
      "core_contribution": "The paper provides a graphical state-machine modeling tool that generates trace analyses and integrates them into Trace Compass, reducing the need to hand-code state providers or XML analyses.",
      "tags": [
        "trace-analysis",
        "trace-visualization",
        "trace-abstraction",
        "performance-analysis"
      ],
      "keywords": [
        "state-machine traces",
        "trace indexing",
        "special-purpose database",
        "Gantt chart",
        "XY view",
        "trace filtering"
      ]
    },
    {
      "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": "depgraph",
      "title": "DepGraph: Localizing Performance Bottlenecks in Multi-Core Applications Using Waiting Dependency Graphs and Software Tracing",
      "year": 2020,
      "authors": [
        "Naser Ezzati-Jivan",
        "Quentin Fournier",
        "Michel R. Dagenais",
        "Abdelwahab Hamou-Lhadj"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/depgraph/",
      "canonical_source_url": "https://doi.org/10.1109/SCAM51674.2020.00022",
      "core_contribution": "DepGraph uses system-level tracing to expose the waiting dependencies that divide a task's execution time across threads and hardware resources.",
      "tags": [
        "kernel-tracing",
        "performance-analysis",
        "root-cause-analysis",
        "multicore-systems"
      ],
      "keywords": [
        "waiting dependency graph",
        "system-level tracing",
        "blocking dependencies",
        "performance bottlenecks",
        "thread-resource interactions"
      ]
    },
    {
      "paper_id": "multilevel-trace-lock-contention",
      "title": "Multi-Level Execution Trace Based Lock Contention Analysis",
      "year": 2020,
      "authors": [
        "Majid Rezazadeh",
        "Naser Ezzati-Jivan",
        "Evan Galea",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multilevel-trace-lock-contention/",
      "canonical_source_url": "https://doi.org/10.1109/issrew51248.2020.00068",
      "core_contribution": "The paper extends critical-path analysis across kernel and user-space lock events so contention from futexes, spinlocks, semaphores, and other synchronization mechanisms can be diagnosed without recompiling the application.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "root-cause-analysis"
      ],
      "keywords": [
        "lock contention",
        "multi-level execution traces",
        "LTTng 2.10",
        "Trace Compass 4",
        "LD_PRELOAD",
        "Pthreads",
        "futex",
        "spinlock",
        "semaphore",
        "critical path",
        "state system",
        "Apache",
        "OPcache"
      ]
    },
    {
      "paper_id": "heterogeneous-debugging-dynamic-tracing",
      "title": "Efficient Large-Scale Heterogeneous Debugging Using Dynamic Tracing",
      "year": 2019,
      "authors": [
        "Didier Nadeau",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/heterogeneous-debugging-dynamic-tracing/",
      "canonical_source_url": "https://doi.org/10.1016/j.sysarc.2019.02.016",
      "core_contribution": "The paper redesigns GDB dynamic tracing around LTTng-UST and scalable trace views so multi-threaded CPU/GPU debugging remains usable on heterogeneous many-core systems.",
      "tags": [
        "system-tracing",
        "trace-analysis",
        "performance-analysis",
        "root-cause-analysis"
      ],
      "keywords": [
        "heterogeneous systems",
        "dynamic tracing",
        "large-scale debugging",
        "debugging efficiency",
        "GDB",
        "LTTng-UST",
        "Trace Compass",
        "ROCm-GDB",
        "GPU debugging",
        "pbzip2"
      ]
    },
    {
      "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": "efficient-cloud-tracing-levels",
      "title": "Efficient cloud tracing: From very high level to very low level",
      "year": 2018,
      "authors": [
        "Yves J. Bationo",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-cloud-tracing-levels/",
      "canonical_source_url": "https://doi.org/10.1109/ICCE.2018.8326353",
      "core_contribution": "The paper correlates OpenStack Nova, QEMU/KVM, and host/guest kernel evidence to diagnose live-migration behavior across cloud-system layers.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "performance-analysis",
        "observability"
      ],
      "keywords": [
        "cloud tracing",
        "high-level tracing",
        "low-level tracing",
        "cross-layer observability",
        "OpenStack Nova",
        "QEMU",
        "KVM",
        "Trace Compass",
        "live migration"
      ]
    },
    {
      "paper_id": "high-latency-cause-detection-multilevel-analysis",
      "title": "High latency cause detection using multilevel dynamic analysis",
      "year": 2018,
      "authors": [
        "Naser Ezzati-Jivan",
        "Genevieve Bastien",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/high-latency-cause-detection-multilevel-analysis/",
      "canonical_source_url": "https://doi.org/10.1109/SYSCON.2018.8369613",
      "core_contribution": "The paper correlates PHP user-space events with LTTng kernel events in a unified multilevel model to explain high-latency web requests.",
      "tags": [
        "system-tracing",
        "latency-analysis",
        "root-cause-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "high latency",
        "dynamic analysis",
        "multilevel analysis",
        "latency causes",
        "LTTng",
        "LTTng-UST",
        "Trace Compass",
        "OPcache contention"
      ]
    },
    {
      "paper_id": "declarative-framework-stateful-trace-analysis",
      "title": "A declarative framework for stateful analysis of execution traces",
      "year": 2017,
      "authors": [
        "Florian Wininger",
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/declarative-framework-stateful-trace-analysis/",
      "canonical_source_url": "https://doi.org/10.1007/s11219-016-9311-0",
      "core_contribution": "The framework lets analysts define stateful trace analyses declaratively, using a generic state model and XML specifications that can drive storage, filtering, and visualization across trace formats.",
      "tags": [
        "trace-analysis",
        "trace-abstraction",
        "performance-analysis",
        "system-tracing",
        "trace-visualization"
      ],
      "keywords": [
        "declarative trace analysis",
        "stateful analysis",
        "execution traces",
        "LTTng",
        "ETW",
        "State History Tree",
        "XML",
        "trace filtering",
        "Gantt chart",
        "critical-path analysis"
      ]
    },
    {
      "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": "multi-scale-navigation-of-large-trace-data-a-survey",
      "title": "Multi-scale navigation of large trace data: A survey",
      "year": 2017,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multi-scale-navigation-of-large-trace-data-a-survey/",
      "canonical_source_url": "https://doi.org/10.1002/cpe.4068",
      "core_contribution": "The survey provides a taxonomy and requirements-oriented comparison of techniques for collecting, abstracting, analyzing, visualizing, and navigating large execution traces.",
      "tags": [
        "trace-visualization",
        "trace-abstraction",
        "kernel-tracing",
        "performance-analysis",
        "system-tracing"
      ],
      "keywords": [
        "trace navigation",
        "multi-scale analysis",
        "trace visualization",
        "content abstraction",
        "metric abstraction",
        "visual abstraction",
        "resource abstraction",
        "semantic zoom",
        "focus-plus-context",
        "Trace Compass",
        "Vampir",
        "Jumpshot",
        "SLOG",
        "R-tree",
        "quadtree",
        "State History Tree"
      ]
    },
    {
      "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": "multiscale-navigation-large-trace-data",
      "title": "Multiscale Navigation in Large Trace Data",
      "year": 2014,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multiscale-navigation-large-trace-data/",
      "canonical_source_url": "https://doi.org/10.1109/CCECE.2014.6901019",
      "core_contribution": "The paper presents multiscale trace navigation that links raw kernel events to system-call, synthetic, and fault/alert abstractions in a zoomable timeline.",
      "tags": [
        "trace-visualization",
        "trace-abstraction",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "large trace data",
        "multiscale navigation",
        "zoomable timeline",
        "semantic zoom",
        "physical zoom",
        "trace visualization"
      ]
    },
    {
      "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": "efficient-model-query-visualize-system-states-traces",
      "title": "Efficient Model to Query and Visualize the System States Extracted from Trace Data",
      "year": 2013,
      "authors": [
        "Alexandre Montplaisir",
        "Naser Ezzati-Jivan",
        "Florian Wininger",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-model-query-visualize-system-states-traces/",
      "canonical_source_url": "https://doi.org/10.1007/978-3-642-40787-1_13",
      "core_contribution": "The paper introduces a disk-backed state-history model that incrementally converts trace events into queryable state intervals, enabling scalable timestamp/state queries and visualization without replaying the entire trace.",
      "tags": [
        "trace-abstraction",
        "trace-visualization",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "system states",
        "trace queries",
        "trace visualization",
        "interval data",
        "tree-based storage",
        "online analysis",
        "offline analysis"
      ]
    },
    {
      "paper_id": "multilevel-label-placement-execution-trace-events",
      "title": "Multilevel Label Placement for Execution Trace Events",
      "year": 2013,
      "authors": [
        "Naser Ezzati-Jivan",
        "Alireza Shameli-Sendi",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/multilevel-label-placement-execution-trace-events/",
      "canonical_source_url": "https://doi.org/10.1109/CCECE.2013.6567826",
      "core_contribution": "The paper develops a multilevel trace-label placement algorithm that preserves readable, semantically meaningful labels while users zoom and pan across dense execution-trace views.",
      "tags": [
        "trace-visualization",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "execution trace events",
        "label placement",
        "trace visualization",
        "overlap avoidance",
        "event labels",
        "CCECE 2013"
      ]
    },
    {
      "paper_id": "a-stateful-approach-to-generate-synthetic-events-from-kernel-traces",
      "title": "A Stateful Approach to Generate Synthetic Events from Kernel Traces",
      "year": 2012,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/a-stateful-approach-to-generate-synthetic-events-from-kernel-traces/",
      "canonical_source_url": "https://doi.org/10.1155/2012/140368",
      "core_contribution": "The paper introduces a stateful trace-abstraction method that converts low-level kernel events into semantic and synthetic events using modeled resource state and finite-state patterns.",
      "tags": [
        "kernel-tracing",
        "system-tracing",
        "trace-abstraction",
        "trace-analysis",
        "performance-analysis"
      ],
      "keywords": [
        "stateful trace abstraction",
        "synthetic events",
        "semantic events",
        "modeled state",
        "finite-state machine",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "XML pattern library",
        "partial trace abstraction",
        "fork bomb",
        "SYN flood",
        "port scanning"
      ]
    },
    {
      "paper_id": "efficient-analysis-multi-core-system-tracing-data",
      "title": "An Efficient Analysis Approach for Multi-Core System Tracing Data",
      "year": 2012,
      "authors": [
        "Naser Ezzati-Jivan",
        "Michel R. Dagenais"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-analysis-multi-core-system-tracing-data/",
      "canonical_source_url": "https://doi.org/10.2316/P.2012.790-053",
      "core_contribution": "The paper introduces a stateful trace-abstraction architecture that shares modeled system state and intermediate computation across concurrent patterns to reduce the cost of analyzing large Linux kernel traces.",
      "tags": [
        "kernel-tracing",
        "lttng",
        "trace-analysis",
        "trace-abstraction",
        "trace-filtering",
        "performance-analysis",
        "multicore-systems"
      ],
      "keywords": [
        "multi-core tracing",
        "LTTng",
        "Linux kernel 2.6.38.6",
        "stateful trace analysis",
        "trace abstraction",
        "EventMapper",
        "modeled state",
        "semantic events",
        "synthetic event generation",
        "State Machine Language",
        "pattern library",
        "partial trace abstraction",
        "SYN flood",
        "fork bomb",
        "kernel trace",
        "Java trace analyzer",
        "computation sharing",
        "storage efficiency"
      ]
    },
    {
      "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"
      ]
    }
  ]
}