{"id":"https://openalex.org/W7152682120","doi":"https://doi.org/10.48550/arxiv.2604.06448","title":"From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures","display_name":"From Load Tests to Live Streams: Graph Embedding-Based Anomaly Detection in Microservice Architectures","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152682120","doi":"https://doi.org/10.48550/arxiv.2604.06448"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06448","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06448","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011657578","display_name":"Srinidhi Madabhushi","orcid":"https://orcid.org/0000-0003-0117-8911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madabhushi, Srinidhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133294057","display_name":"Pranesh Vyas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vyas, Pranesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133250222","display_name":"Swathi Vaidyanathan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vaidyanathan, Swathi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133288381","display_name":"Mayur Kurup","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurup, Mayur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133251599","display_name":"Elliott Nash","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nash, Elliott","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133309554","display_name":"Yegor Silyutin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Silyutin, Yegor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9320999979972839,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.9320999979972839,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.017999999225139618,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.00430000014603138,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6764000058174133},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5522000193595886},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4652000069618225},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46059998869895935},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.3546000123023987},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.34689998626708984},{"id":"https://openalex.org/keywords/load-testing","display_name":"Load testing","score":0.3343999981880188},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.3301999866962433}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6861000061035156},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6764000058174133},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5522000193595886},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46059998869895935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4154999852180481},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3984000086784363},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.34689998626708984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3386000096797943},{"id":"https://openalex.org/C48460631","wikidata":"https://www.wikidata.org/wiki/Q4311799","display_name":"Load testing","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C184992742","wikidata":"https://www.wikidata.org/wiki/Q7243229","display_name":"Prime (order theory)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.3197000026702881},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2694000005722046},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2779242764","wikidata":"https://www.wikidata.org/wiki/Q129","display_name":"Thursday","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06448","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06448","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.46101444959640503}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Prime":[0,147],"Video":[1,148],"regularly":[2],"conducts":[3],"load":[4,91],"tests":[5,35],"to":[6,46],"simulate":[7],"the":[8],"viewer":[9],"traffic":[10],"spikes":[11],"seen":[12],"during":[13],"live":[14],"events":[15,26],"such":[16,27],"as":[17,21,23,28],"Thursday":[18],"Night":[19],"Football":[20],"well":[22],"video-on-demand":[24],"(VOD)":[25],"Rings":[29],"of":[30],"Power.":[31],"While":[32],"these":[33],"stress":[34],"validate":[36],"system":[37,56,97],"capacity,":[38],"they":[39],"can":[40],"sometimes":[41],"miss":[42],"service":[43,78],"behaviors":[44],"unique":[45],"real":[47],"event":[48,94],"traffic.":[49],"We":[50,109],"present":[51],"a":[52,68,112,157],"graph-based":[53],"anomaly":[54,115],"detection":[55,107],"that":[57,101,121],"identifies":[58,98],"under-represented":[59],"services":[60,100],"using":[61],"unsupervised":[62],"node-level":[63],"graph":[64],"embeddings.":[65,95],"Built":[66],"on":[67,87],"GCN-GAE,":[69],"our":[70],"approach":[71],"learns":[72],"structural":[73],"representations":[74],"from":[75],"directed,":[76],"weighted":[77],"graphs":[79],"at":[80],"minute-level":[81],"resolution":[82],"and":[83,93,104,126,154],"flags":[84],"anomalies":[85],"based":[86],"cosine":[88],"similarity":[89],"between":[90],"test":[92],"The":[96],"incident-related":[99],"are":[102],"documented":[103],"demonstrates":[105,143],"early":[106],"capability.":[108],"also":[110,150],"introduce":[111],"preliminary":[113],"synthetic":[114],"injection":[116],"framework":[117,142],"for":[118,159],"controlled":[119],"evaluation":[120],"show":[122],"promising":[123],"precision":[124],"(96%)":[125],"low":[127],"false":[128],"positive":[129],"rate":[130],"(0.08%),":[131],"though":[132],"recall":[133],"(58%)":[134],"remains":[135],"limited":[136],"under":[137],"conservative":[138],"propagation":[139],"assumptions.":[140],"This":[141],"practical":[144],"utility":[145],"within":[146],"while":[149],"surfacing":[151],"methodological":[152],"lessons":[153],"directions,":[155],"providing":[156],"foundation":[158],"broader":[160],"application":[161],"across":[162],"microservice":[163],"ecosystems.":[164]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-10T00:00:00"}
