{ "schema_version": "0.6.0", "paper_id": "efficient-communication-performance-antipattern-detection-microservices", "page_url": "https://naser.github.io/research-publications/papers/efficient-communication-performance-antipattern-detection-microservices/", "title": "Efficient Detection of Communication-related Performance Anti-patterns in Microservices", "title_variants": [], "authors": [ "Masoumeh Nourollahi", "Naser Ezzati-Jivan", "Adel Belkheiri", "Michel Dagenais" ], "author_details": [ { "name": "Masoumeh Nourollahi", "orcid": null, "profile_url": null }, { "name": "Naser Ezzati-Jivan", "orcid": "https://orcid.org/0000-0003-1435-6297", "profile_url": "https://naser.github.io/" }, { "name": "Adel Belkheiri", "orcid": null, "profile_url": null }, { "name": "Michel Dagenais", "orcid": null, "profile_url": null } ], "publication": { "year": 2025, "venue": "Authorea preprint", "type": "preprint", "publication_date": "2025-08-16", "online_date": null, "print_date": null, "volume": null, "issue": null, "pages": null, "article_number": null, "publisher": "Wiley", "issn": [], "isbn": [], "crossref_type": "posted-content" }, "publication_type": "preprint", "status": "preprint_with_public_full_text", "canonical_source_url": "https://doi.org/10.22541/au.175533132.24109345/v1", "source_record_id": "efficient-detection-of-communication-related-performance-anti-patterns-in-microservices-30dc5b1051", "identifiers": { "doi": "10.22541/au.175533132.24109345/v1" }, "abstract": "The preprint combines selected communication-related system-call traces with distributed observability traces to detect Blob and Empty Semi Trucks anti-patterns in microservices. It reports supervised, semi-supervised, and unsupervised settings on DeathStarBench scenarios.", "abstract_source": "Authorea public preprint abstract, paraphrased; the preprint is not peer-reviewed.", "abstract_available": true, "scholar_eligibility": { "eligible": false, "basis": "not-eligible", "note": "The page is a discovery record; it does not claim Google Scholar article-host eligibility." }, "description": "The preprint combines selected communication-related system-call traces with distributed observability traces to detect Blob and Empty Semi Trucks anti-patterns in microservices. It reports supervised, semi-supervised, and unsupervised settings on DeathStarBench scenarios.", "evidence_level": "full-text-reviewed", "evidence": { "source_basis": "full-text-reviewed", "coverage": "material paper sections", "summary_origin": "AI-assisted catalog editorial summary", "review_status": "catalog-reviewed; paper-author approval pending", "verified_on": "2026-08-09", "sources": [ { "note": "Remote full preprint read: pdf-evidence/notes/parallel-batch-01-efficient-communication-anti-patterns.md" }, { "note": "Authorea DOI: https://doi.org/10.22541/au.175533132.24109345/v1" }, { "note": "Public preprint PDF: https://d197for5662m48.cloudfront.net/documents/publicationstatus/274238/preprint_pdf/0c308204518850728852b7cf01be90f6.pdf" } ] }, "summary": { "core_contribution": "The preprint combines communication-related kernel/system-call events with distributed traces to detect communication performance anti-patterns in microservices.", "problem": "Communication-related performance antipatterns in microservices can be hidden across user-level request traces and lower-level communication activity. The paper targets detection of Blob and Empty-semi-trucks while limiting collection overhead.", "method": "The workflow combines distributed observability traces with selected communication-related system calls, correlates user-space and kernel events, aggregates features, and supports supervised, semi-supervised, and unsupervised learning. The reported setup uses LTTng, Trace Compass, Jaeger-client instrumentation, liblttng-ust, and kernel tracing, with offline training and online detection.", "findings": "The evaluation uses DeathStarBench with 14 clean/noisy scenarios. The abstract reports up to 91% accuracy, 2.74% data-collection overhead, and more than 80% agreement with manual analysis. The later discussion reports 63% for the unsupervised setting and higher supervised performance, with semi-supervised learning positioned as a labeling-cost/accuracy compromise.", "limitations": "Evidence is a remote-read public preprint, not a locally hashed or peer-reviewed version. The approach depends on the LTTng/Jaeger/Trace Compass stack and communication-specific calls such as recvfrom/recvmsg/recvmmsg and sendto/sendmsg/sendmmsg. It is evaluated on controlled DeathStarBench scenarios, not proprietary industrial systems. Tool versions, OS/kernel, hardware, runtime, and replication details are unknown in the retained evidence note.", "future_work": "Evaluate with industrial partners, add I/O-related system-call families, and investigate topic-modeling or generative-AI extensions." }, "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" ], "versions": [ { "id": "published-version", "label": "Published version", "relation": "preprint", "title": "Efficient Detection of Communication-related Performance Anti-patterns in Microservices", "url": "https://doi.org/10.22541/au.175533132.24109345/v1", "pdf_url": null, "status": "preprint", "canonical_for_citation": true }, { "id": "public-preprint-pdf", "label": "Public preprint PDF", "relation": "preprint", "title": "Efficient Detection of Communication-related Performance Anti-patterns in Microservices", "url": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/274238/preprint_pdf/0c308204518850728852b7cf01be90f6.pdf", "pdf_url": "https://d197for5662m48.cloudfront.net/documents/publicationstatus/274238/preprint_pdf/0c308204518850728852b7cf01be90f6.pdf", "status": "public_full_text", "canonical_for_citation": false } ], "access": { "status": "preprint_with_public_full_text", "note": "This is an Authorea preprint explicitly identified as not peer-reviewed. The DOI is the preprint citation target; the external PDF is linked but not redistributed here.", "license": null }, "resources": { "code": null, "data": null, "slides": null, "demo": null }, "citation_guidance": { "when_to_cite": "Cite this paper when your work uses or compares combining distributed spans with communication-related kernel/user system calls to detect microservice performance antipatterns.", "points": [ "combining distributed spans with communication-related kernel/user system calls to detect microservice performance antipatterns.", "the Blob and Empty-semi-truck target classes and the 14-scenario DeathStarBench evaluation.", "the supervised/semi-supervised/unsupervised comparison and its reported collection-overhead/manual-agreement measurements.", "with an explicit preprint and controlled-testbed caveat; do not present it as peer-reviewed industrial evidence." ], "canonical_version_id": "published-version" }, "provenance": { "metadata_verified_on": "2026-08-09", "metadata_source": [ "Remote full preprint read: pdf-evidence/notes/parallel-batch-01-efficient-communication-anti-patterns.md", "Authorea DOI: https://doi.org/10.22541/au.175533132.24109345/v1", "Public preprint PDF: https://d197for5662m48.cloudfront.net/documents/publicationstatus/274238/preprint_pdf/0c308204518850728852b7cf01be90f6.pdf" ], "summary_written_by": "AI-assisted", "summary_verified_by": "full-text-grounded catalog review; author approval pending", "linked_preprint_record": null, "author_order_note": null }, "batch": { "phase": 2, "batch_label": "expanded forty-paper release", "status": "included_in_expanded_catalog", "selected_at": "2026-08-09" } }