{"id":"https://openalex.org/W7116438034","doi":"https://doi.org/10.1145/3754598.3754643","title":"A High-Accuracy Sketch for Measuring Low-Entropy Flows in Distributed AI Training","display_name":"A High-Accuracy Sketch for Measuring Low-Entropy Flows in Distributed AI Training","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7116438034","doi":"https://doi.org/10.1145/3754598.3754643"},"language":null,"primary_location":{"id":"doi:10.1145/3754598.3754643","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754643","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3754598.3754643","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120938442","display_name":"Jin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210149102","display_name":"Sanya University","ror":"https://ror.org/04fa2qd52","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210149102"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jin Wang","raw_affiliation_strings":["Sanya Institute of Hunan University of Science and Technology, Sanya,Hainan, China"],"raw_orcid":"https://orcid.org/0000-0001-5473-8738","affiliations":[{"raw_affiliation_string":"Sanya Institute of Hunan University of Science and Technology, Sanya,Hainan, China","institution_ids":["https://openalex.org/I4210149102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081179593","display_name":"Chenye Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenye Zhu","raw_affiliation_strings":["Hunan University of Science and Technology, Xiangtan,Hunan, China"],"raw_orcid":"https://orcid.org/0009-0002-9674-0523","affiliations":[{"raw_affiliation_string":"Hunan University of Science and Technology, Xiangtan,Hunan, China","institution_ids":["https://openalex.org/I121296143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120924364","display_name":"Jinbin Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbin Hu","raw_affiliation_strings":["Changsha University of Science and Technology, Changsha,Hunan, China"],"raw_orcid":"https://orcid.org/0000-0001-8216-9683","affiliations":[{"raw_affiliation_string":"Changsha University of Science and Technology, Changsha,Hunan, China","institution_ids":["https://openalex.org/I56934997"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120938442"],"corresponding_institution_ids":["https://openalex.org/I4210149102"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.624066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"53","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.17159999907016754,"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":0.17159999907016754,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.1518000066280365,"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/T10714","display_name":"Software-Defined Networks and 5G","score":0.09719999879598618,"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/sketch","display_name":"Sketch","score":0.8007000088691711},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.671999990940094},{"id":"https://openalex.org/keywords/queue","display_name":"Queue","score":0.6039000153541565},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5364000201225281},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5266000032424927},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4950000047683716}],"concepts":[{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.8007000088691711},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7882000207901001},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.671999990940094},{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.6039000153541565},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5364000201225281},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4950000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46369999647140503},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37119999527931213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3269999921321869},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3754598.3754643","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754643","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3754598.3754643","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3754598.3754643","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 54th International Conference on Parallel Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.4106888771057129,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2080234606","https://openalex.org/W2479510968","https://openalex.org/W2753629899","https://openalex.org/W2799200478","https://openalex.org/W2808923818","https://openalex.org/W2834288129","https://openalex.org/W2950087731","https://openalex.org/W2969998124","https://openalex.org/W3007098654","https://openalex.org/W3037009841","https://openalex.org/W3080797160","https://openalex.org/W3081247648","https://openalex.org/W3176348316","https://openalex.org/W3177067869","https://openalex.org/W4235107506","https://openalex.org/W4283218652","https://openalex.org/W4283323602","https://openalex.org/W4289533821","https://openalex.org/W4289534029","https://openalex.org/W4292967977","https://openalex.org/W4313547918","https://openalex.org/W4380433141","https://openalex.org/W4380433166","https://openalex.org/W4380433170","https://openalex.org/W4384304134","https://openalex.org/W4385270409","https://openalex.org/W4385270442","https://openalex.org/W4385283793","https://openalex.org/W4386233238","https://openalex.org/W4389988651","https://openalex.org/W4400909785","https://openalex.org/W4401176258","https://openalex.org/W4401176521","https://openalex.org/W4401508595","https://openalex.org/W4404576895"],"related_works":[],"abstract_inverted_index":{"Distributed":[0],"AI":[1,87,101],"training":[2],"generates":[3],"unique":[4],"low-entropy":[5,59,86],"flow":[6,20,120],"patterns":[7],"with":[8,21,61,90],"predictable,":[9],"singular":[10],"and":[11,71,82,125],"repetitive":[12],"flows":[13,60,73],"that":[14,117],"differ":[15],"fundamentally":[16],"from":[17],"traditional":[18],"network":[19,31],"heavy-tailed":[22],"distributions.":[23],"While":[24],"sketch-based":[25],"methods":[26],"are":[27],"widely":[28],"used":[29],"for":[30,57,99,108],"measurement,":[32],"existing":[33],"approaches":[34],"fail":[35],"to":[36,69,132],"exploit":[37],"these":[38],"distinctive":[39],"characteristics,":[40],"resulting":[41],"in":[42],"poor":[43],"measurement":[44,97],"accuracy.":[45],"To":[46],"address":[47],"this":[48,50],"issue,":[49],"paper":[51],"proposes":[52],"FP-Sketch,":[53],"a":[54,66],"high-accuracy":[55],"sketch":[56],"measuring":[58],"Flow":[62],"Prediction.":[63],"FP-Sketch":[64,109,118],"utilizes":[65],"staging":[67],"queue":[68],"predict":[70],"classify":[72],"of":[74,85],"different":[75],"sizes,":[76],"thereby":[77],"leveraging":[78],"the":[79,133],"singular,":[80],"repetitive,":[81],"predictable":[83],"nature":[84],"flows.":[88],"Combined":[89],"hierarchical":[91],"storage,":[92],"our":[93],"method":[94],"achieves":[95],"superior":[96],"precision":[98],"distributed":[100],"workloads.":[102],"We":[103],"establish":[104],"rigorous":[105],"error":[106,122],"bounds":[107],"through":[110],"theoretical":[111],"analysis.":[112],"The":[113],"experimental":[114],"results":[115],"show":[116],"reduces":[119],"estimation":[121],"by":[123,129],"38.6%":[124],"improves":[126],"insertion":[127],"throughput":[128],"49.4%":[130],"compared":[131],"state-of-the-art":[134],"alternatives.":[135]},"counts_by_year":[],"updated_date":"2025-12-21T02:06:08.432651","created_date":"2025-12-21T00:00:00"}