{"id":"https://openalex.org/W4415124805","doi":"https://doi.org/10.1109/icnp65844.2025.11192327","title":"MEC-Sketch: Memory-Efficient Per-Flow Cardinality Measurement in High-Speed Networks","display_name":"MEC-Sketch: Memory-Efficient Per-Flow Cardinality Measurement in High-Speed Networks","publication_year":2025,"publication_date":"2025-09-22","ids":{"openalex":"https://openalex.org/W4415124805","doi":"https://doi.org/10.1109/icnp65844.2025.11192327"},"language":"en","primary_location":{"id":"doi:10.1109/icnp65844.2025.11192327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnp65844.2025.11192327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 33rd International Conference on Network Protocols (ICNP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108991933","display_name":"Kejun Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kejun Guo","raw_affiliation_strings":["Northeastern University,Shenyang,China,110819"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China,110819","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083598234","display_name":"Fuliang Li","orcid":"https://orcid.org/0000-0001-9782-0053"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuliang Li","raw_affiliation_strings":["Northeastern University,Shenyang,China,110819"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China,110819","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101946570","display_name":"Yunjie Zhang","orcid":"https://orcid.org/0000-0002-7702-310X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunjie Zhang","raw_affiliation_strings":["Northeastern University,Shenyang,China,110819"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China,110819","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haorui Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haorui Wan","raw_affiliation_strings":["Northeastern University,Shenyang,China,110819"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China,110819","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020156280","display_name":"Jiaxing Shen","orcid":"https://orcid.org/0000-0002-0833-0288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaxing Shen","raw_affiliation_strings":["Lingnan University,Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lingnan University,Hong Kong","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100326915","display_name":"Xingwei Wang","orcid":"https://orcid.org/0000-0003-2856-4716"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingwei Wang","raw_affiliation_strings":["Northeastern University,Shenyang,China,110819"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeastern University,Shenyang,China,110819","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9666000008583069,"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/T10138","display_name":"Network Traffic and Congestion Control","score":0.9666000008583069,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.9520000219345093,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9473000168800354,"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/cardinality","display_name":"Cardinality (data modeling)","score":0.9580000042915344},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7728999853134155},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5023999810218811},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.42660000920295715},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.29120001196861267}],"concepts":[{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.9580000042915344},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7728999853134155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5853000283241272},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.484499990940094},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4528999924659729},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.42660000920295715},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32910001277923584},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2833000123500824},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnp65844.2025.11192327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnp65844.2025.11192327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 33rd International Conference on Network Protocols (ICNP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320328618","display_name":"Lingnan University","ror":"https://ror.org/0563pg902"},{"id":"https://openalex.org/F4320329895","display_name":"Liaoning Revitalization Talents Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2008365755","https://openalex.org/W2034302520","https://openalex.org/W2078345533","https://openalex.org/W2080234606","https://openalex.org/W2104692292","https://openalex.org/W2130873367","https://openalex.org/W2134166219","https://openalex.org/W2144982963","https://openalex.org/W2148665392","https://openalex.org/W2152374361","https://openalex.org/W2168305032","https://openalex.org/W2205557629","https://openalex.org/W2327315875","https://openalex.org/W2798945787","https://openalex.org/W2808923818","https://openalex.org/W2834288129","https://openalex.org/W2919413299","https://openalex.org/W2969998124","https://openalex.org/W3046981145","https://openalex.org/W3047429575","https://openalex.org/W3145761337","https://openalex.org/W3197219182","https://openalex.org/W3199406030","https://openalex.org/W4244556018","https://openalex.org/W4249843299","https://openalex.org/W4380433166","https://openalex.org/W4386396862","https://openalex.org/W4402042950","https://openalex.org/W4402896858","https://openalex.org/W4404238466","https://openalex.org/W4405440258","https://openalex.org/W4405811844","https://openalex.org/W4414169964"],"related_works":[],"abstract_inverted_index":{"Per-Flow":[0],"cardinality":[1,16,49,54,99,138,161,220],"measurement":[2,55,142],"in":[3,65,112,180,187,208],"high-speed":[4],"networks":[5],"is":[6],"essential":[7],"for":[8,128,140,167,218],"network":[9,67,113,199],"security":[10],"and":[11,59,85,132,170,184,215,222],"traffic":[12],"analysis":[13],"applications.":[14],"Flow":[15],"refers":[17],"to":[18,158],"the":[19,29,71,105,154,181,188],"number":[20,30],"of":[21,31,75,109,143,210],"distinct":[22],"elements":[23],"within":[24],"a":[25,37,97,117,120,124,133],"flow,":[26],"such":[27],"as":[28],"unique":[32],"destination":[33],"IPs":[34],"associated":[35],"with":[36,56,81,88,160],"given":[38,70],"source":[39],"IP.":[40],"While":[41],"extensive":[42],"research":[43],"has":[44],"been":[45],"conducted":[46],"on":[47,197],"single-flow":[48],"estimation,":[50],"achieving":[51],"accurate":[52],"per-flow":[53],"real-time":[57,168],"performance":[58,217],"low":[60],"memory":[61,213],"overhead":[62],"remains":[63],"challenging":[64],"large-scale":[66],"environments,":[68],"particularly":[69],"highly":[72],"skewed":[73,107],"distribution":[74,108],"flow":[76,110,193],"cardinalities":[77,83,90,111],"where":[78],"mouse":[79,144],"flows":[80,87],"smaller":[82],"dominate,":[84],"elephant":[86],"larger":[89],"are":[91],"fewer.":[92],"This":[93],"paper":[94],"introduces":[95],"MEC-Sketch,":[96],"memory-efficient":[98,141],"estimation":[100,211,221],"data":[101],"structure":[102],"that":[103,163,202],"leverages":[104],"inherently":[106],"traffic.":[114],"MEC-Sketch":[115,203],"employs":[116],"dual-component":[118],"architecture:":[119],"heavy":[121,182],"part":[122,135,183,190],"utilizing":[123],"majority":[125,155],"vote":[126,156],"algorithm":[127],"precise":[129],"super-spreader":[130,223],"detection,":[131],"light":[134,189],"implementing":[136,172],"compact":[137],"estimators":[139,162,179,186],"flows.":[145],"We":[146],"address":[147],"two":[148],"fundamental":[149],"technical":[150],"challenges:":[151],"(1)":[152],"adapting":[153],"algorithms":[157],"operate":[159],"lack":[164],"native":[165],"support":[166],"queries,":[169],"(2)":[171],"an":[173],"effective":[174],"mapping":[175],"strategy":[176],"between":[177],"large":[178],"small":[185],"during":[191],"elephant-mouse":[192],"separation.":[194],"Comprehensive":[195],"evaluations":[196],"real-world":[198],"traces":[200],"demonstrate":[201],"significantly":[204],"outperforms":[205],"state-of-the-art":[206],"solutions":[207],"terms":[209],"accuracy,":[212],"efficiency,":[214],"computational":[216],"both":[219],"detection":[224],"tasks.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-14T00:00:00"}
