{"id":"https://openalex.org/W3036348624","doi":"https://doi.org/10.1145/3391812.3396268","title":"Data-driven Learning to Predict WAN Network Traffic","display_name":"Data-driven Learning to Predict WAN Network Traffic","publication_year":2020,"publication_date":"2020-06-16","ids":{"openalex":"https://openalex.org/W3036348624","doi":"https://doi.org/10.1145/3391812.3396268","mag":"3036348624"},"language":"en","primary_location":{"id":"doi:10.1145/3391812.3396268","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391812.3396268","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391812.3396268","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3391812.3396268","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036303497","display_name":"Nandini Krishnaswamy","orcid":null},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nandini Krishnaswamy","raw_affiliation_strings":["Lawrence Berkeley National Lab, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Lawrence Berkeley National Lab, Berkeley, CA, USA","institution_ids":["https://openalex.org/I148283060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101652202","display_name":"Mariam Kiran","orcid":"https://orcid.org/0000-0001-5775-0855"},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mariam Kiran","raw_affiliation_strings":["Lawrence Berkeley National Lab, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Lawrence Berkeley National Lab, Berkeley, CA, USA","institution_ids":["https://openalex.org/I148283060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006303811","display_name":"K. Ranjith Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunal Singh","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053458810","display_name":"Bashir Mohammed","orcid":"https://orcid.org/0000-0002-0796-2195"},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bashir Mohammed","raw_affiliation_strings":["Lawrence Berkeley National Lab, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Lawrence Berkeley National Lab, Berkeley, CA, USA","institution_ids":["https://openalex.org/I148283060"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036303497"],"corresponding_institution_ids":["https://openalex.org/I148283060"],"apc_list":null,"apc_paid":null,"fwci":2.4268,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.89528177,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10138","display_name":"Network Traffic and Congestion Control","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9991000294685364,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.8120280504226685},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8061187267303467},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.571140468120575},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5636488795280457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5560306906700134},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5546009540557861},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5395779609680176},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5178699493408203},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.4967203736305237},{"id":"https://openalex.org/keywords/traffic-classification","display_name":"Traffic classification","score":0.46318554878234863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4587867558002472},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.4458438456058502},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.29976439476013184},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.25330376625061035},{"id":"https://openalex.org/keywords/quality-of-service","display_name":"Quality of service","score":0.0925869345664978}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8120280504226685},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061187267303467},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.571140468120575},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5636488795280457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5560306906700134},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5546009540557861},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5395779609680176},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5178699493408203},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.4967203736305237},{"id":"https://openalex.org/C169988225","wikidata":"https://www.wikidata.org/wiki/Q7832484","display_name":"Traffic classification","level":3,"score":0.46318554878234863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4587867558002472},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.4458438456058502},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.29976439476013184},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.25330376625061035},{"id":"https://openalex.org/C5119721","wikidata":"https://www.wikidata.org/wiki/Q220501","display_name":"Quality of service","level":2,"score":0.0925869345664978},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3391812.3396268","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391812.3396268","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391812.3396268","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3391812.3396268","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3391812.3396268","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3391812.3396268","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5199999809265137,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G840158998","display_name":null,"funder_award_id":"FP00006145","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3036348624.pdf","grobid_xml":"https://content.openalex.org/works/W3036348624.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2083209848","https://openalex.org/W2095705004","https://openalex.org/W2110242546","https://openalex.org/W2128889245","https://openalex.org/W2130942839","https://openalex.org/W2164096531","https://openalex.org/W2462192046","https://openalex.org/W2489782956","https://openalex.org/W2585034481","https://openalex.org/W2593182953","https://openalex.org/W3013430759","https://openalex.org/W4236202168","https://openalex.org/W6679436768"],"related_works":["https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2146461990","https://openalex.org/W4312309719","https://openalex.org/W4391216528","https://openalex.org/W2980748541","https://openalex.org/W4399581288","https://openalex.org/W4313123484"],"abstract_inverted_index":{"In":[0,30],"this":[1,31],"paper,":[2,32],"we":[3,33],"explore":[4],"both":[5],"statistical":[6,66],"and":[7,26,57,70,72,83,88],"deep":[8],"learning":[9],"approaches":[10,75],"for":[11],"multi-step":[12],"predictions":[13],"in":[14],"WAN":[15,41],"traffic":[16,20,53],"traces.":[17],"Estimating":[18],"future":[19],"can":[21],"help":[22],"improve":[23],"link":[24],"usage":[25],"optimize":[27],"bandwidth":[28],"utilization.":[29],"study":[34],"real":[35,40],"network":[36,79],"traces":[37],"from":[38],"a":[39],"research":[42],"network.":[43],"We":[44,63],"use":[45],"Fourier":[46],"analysis":[47],"to":[48,86],"present":[49],"variation":[50],"among":[51],"the":[52,90],"traces,":[54],"extracting":[55],"daily":[56],"weekly":[58],"peak":[59],"frequencies":[60],"per":[61],"trace.":[62],"also":[64],"develop":[65],"time-series":[67],"methods,":[68],"ARIMA":[69],"Holt-Winters,":[71],"three":[73],"LSTM-based":[74],"with":[76],"various":[77],"neural":[78],"architectures":[80],"(Simple,":[81],"Stacked":[82],"S2S":[84],"LSTM),":[85],"forecast":[87],"compare":[89],"accuracies":[91],"between":[92],"them.":[93]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}