{
  "schema_version": "0.1.0",
  "record_type": "research-topic",
  "topic_id": "microservices",
  "label": "Microservices",
  "title": "Microservices Performance and Reliability Research",
  "description": "Research papers on microservice traces, call graphs, latency, performance, and root-cause analysis.",
  "introduction": "This topic gathers work on understanding multi-service applications through distributed traces, service-invocation graphs, profiling metrics, and performance models. The papers address latency variation, service interactions, anomaly detection, trace reduction, and root-cause localization in microservice environments.",
  "aliases": [
    "microservice systems",
    "microservice performance",
    "distributed service analysis"
  ],
  "search_terms": [
    "microservices",
    "microservice performance",
    "distributed tracing",
    "service call graphs",
    "microservice RCA"
  ],
  "related_topics": [
    "latency-analysis",
    "root-cause-analysis",
    "observability",
    "system-tracing",
    "graph-neural-networks"
  ],
  "canonical_url": "https://threadslab.org/research-publications/topics/microservices.html",
  "paper_count": 12,
  "papers": [
    {
      "paper_id": "care-context-aware-root-cause-identification",
      "title": "CARE: Context Aware Root Cause Identification Using Distributed Traces and Profiling Metrics",
      "year": 2026,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj",
        "James Miller"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/care-context-aware-root-cause-identification/",
      "canonical_source_url": "https://doi.org/10.1109/TSE.2025.3645143",
      "core_contribution": "CARE combines distributed traces and profiling metrics with graph- and spectrum-based analysis to localize performance root causes in microservices.",
      "tags": [
        "system-tracing",
        "microservices",
        "root-cause-analysis",
        "latency-analysis"
      ],
      "keywords": [
        "distributed traces",
        "profiling metrics",
        "context-aware RCA",
        "microservice diagnosis",
        "TrainTicket",
        "spectrum-based fault localization",
        "PageRank",
        "China Mobile Zhejiang"
      ]
    },
    {
      "paper_id": "dtracomp-distributed-trace-comparison",
      "title": "DTraComp: Comparing distributed execution traces for understanding intermittent latency sources",
      "year": 2026,
      "authors": [
        "Maryam Ekhlasi",
        "Fatemeh Faraji Daneshgar",
        "Michel Dagenais",
        "Maxime Lamothe",
        "Naser Ezzati-Jivan",
        "Matthew Khouzam"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/dtracomp-distributed-trace-comparison/",
      "canonical_source_url": "https://doi.org/10.1016/j.jss.2026.112990",
      "core_contribution": "DTraComp is an open-source Eclipse Trace Compass framework that compares groups of distributed requests and attributes span time to user-space, kernel, thread-state, and system-call evidence.",
      "tags": [
        "system-tracing",
        "kernel-tracing",
        "trace-analysis",
        "trace-visualization",
        "latency-analysis",
        "root-cause-analysis",
        "microservices",
        "lttng"
      ],
      "keywords": [
        "DTraComp",
        "distributed trace comparison",
        "OpenTracing",
        "LTTng",
        "LTTng-UST",
        "Eclipse Trace Compass",
        "differential flame graph",
        "span-state attribution",
        "system-call attribution",
        "Waited CPU",
        "Waited Blocked",
        "HotROD",
        "TiDB",
        "Apache Cassandra",
        "Eclipse Theia",
        "JFreeChart",
        "Jaeger",
        "microservice performance"
      ]
    },
    {
      "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": "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": "hybridrca-critical-path-aware-tracing",
      "title": "HybridRCA: Lightweight Critical-Path-Aware Hybrid Tracing for Root-Cause Analysis in Production Microservices",
      "year": 2025,
      "authors": [
        "Maryam Ekhlasi",
        "Arnaud Fiorini",
        "Michel R. Dagenais",
        "Naser Ezzati-Jivan",
        "Maxime Lamothe"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/hybridrca-critical-path-aware-tracing/",
      "canonical_source_url": "https://doi.org/10.1109/icsme64153.2025.00056",
      "core_contribution": "HybridRCA combines critical-path-aware span analysis with targeted kernel metrics to reduce production trace volume while preserving root-cause localization evidence.",
      "tags": [
        "system-tracing",
        "microservices",
        "root-cause-analysis",
        "latency-analysis"
      ],
      "keywords": [
        "critical path",
        "hybrid tracing",
        "production microservices",
        "LTTng",
        "OpenTracing",
        "Personalized PageRank",
        "weighted spectrum-based fault localization",
        "HotROD",
        "TrainTicket",
        "OnlineBoutique",
        "TiDB",
        "SysBench",
        "kernel-level storage"
      ]
    },
    {
      "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": "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": "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": "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": "efficient-unsupervised-latency-culprit-ranking",
      "title": "Efficient Unsupervised Latency Culprit Ranking in Distributed Traces with GNN and Critical Path Analysis",
      "year": 2024,
      "authors": [
        "Mahsa Panahandeh",
        "Naser Ezzati-Jivan",
        "Abdelwahab Hamou-Lhadj",
        "James Miller"
      ],
      "page_url": "https://threadslab.org/research-publications/papers/efficient-unsupervised-latency-culprit-ranking/",
      "canonical_source_url": "https://doi.org/10.1145/3629527.3651841",
      "core_contribution": "The paper combines an unsupervised GraphSAGE model with critical-path-specific latency profiles to detect anomalous requests and rank likely microservice culprits without labelled training data.",
      "tags": [
        "microservices",
        "graph-neural-networks",
        "latency-analysis",
        "root-cause-analysis",
        "trace-analysis"
      ],
      "keywords": [
        "latency culprit ranking",
        "distributed traces",
        "GraphSAGE",
        "graph neural networks",
        "critical path",
        "FIRM dataset",
        "service invocation graph",
        "unsupervised anomaly detection",
        "Top-k ranking"
      ]
    },
    {
      "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": "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"
      ]
    }
  ]
}