{"id":"https://openalex.org/W2109961434","doi":"https://doi.org/10.1109/p2p.2009.5284550","title":"Robust lifetime measurement in large-scale P2P systems with non-stationary arrivals","display_name":"Robust lifetime measurement in large-scale P2P systems with non-stationary arrivals","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2109961434","doi":"https://doi.org/10.1109/p2p.2009.5284550","mag":"2109961434"},"language":"en","primary_location":{"id":"doi:10.1109/p2p.2009.5284550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/p2p.2009.5284550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Ninth International Conference on Peer-to-Peer Computing","raw_type":"proceedings-article"},"type":"article","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/A5057410248","display_name":"Xiaoming Wang","orcid":"https://orcid.org/0000-0003-3340-6122"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaoming Wang","raw_affiliation_strings":["Texas A and M University, College Station, TX, USA","Texas A&M University College Station, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Texas A and M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&M University College Station, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102055784","display_name":"Zhongmei Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhongmei Yao","raw_affiliation_strings":["Texas A and M University, College Station, TX, USA","Texas A&M University College Station, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Texas A and M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&M University College Station, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068944794","display_name":"Yueping Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yueping Zhang","raw_affiliation_strings":["NEC Laboratories of America, Inc., Princeton, NJ, USA","NEC Laboratories America, Inc., Princeton, NJ 08540. USA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories of America, Inc., Princeton, NJ, USA","institution_ids":[]},{"raw_affiliation_string":"NEC Laboratories America, Inc., Princeton, NJ 08540. USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076734500","display_name":"Dmitri Loguinov","orcid":"https://orcid.org/0000-0003-3876-1000"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitri Loguinov","raw_affiliation_strings":["Texas A and M University, College Station, TX, USA","Texas A&M University College Station, 77843, USA"],"affiliations":[{"raw_affiliation_string":"Texas A and M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"Texas A&M University College Station, 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057410248"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":0.7251,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.7435408,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"4673","issue":null,"first_page":"101","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10742","display_name":"Peer-to-Peer Network Technologies","score":0.9998000264167786,"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/T10742","display_name":"Peer-to-Peer Network Technologies","score":0.9998000264167786,"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/T11478","display_name":"Caching and Content Delivery","score":0.9988999962806702,"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/T10138","display_name":"Network Traffic and Congestion Control","score":0.9959999918937683,"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/computer-science","display_name":"Computer science","score":0.7442945837974548},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7101846933364868},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6990201473236084},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6310327053070068},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6182358860969543},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.48040106892585754},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40141212940216064},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36809754371643066},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2925530672073364},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1763160526752472},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1456506848335266},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.0861566960811615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7442945837974548},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7101846933364868},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6990201473236084},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6310327053070068},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6182358860969543},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.48040106892585754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40141212940216064},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36809754371643066},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2925530672073364},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1763160526752472},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1456506848335266},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0861566960811615},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/p2p.2009.5284550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/p2p.2009.5284550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Ninth International Conference on Peer-to-Peer Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.151.944","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://irl.cs.tamu.edu/people/xiliang/papers/p2p2009.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.185.4071","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.185.4071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://irl.cs.tamu.edu/people/xiaoming/papers/p2p09-tr.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W125948722","https://openalex.org/W1499998276","https://openalex.org/W1550768496","https://openalex.org/W1631563397","https://openalex.org/W1677118421","https://openalex.org/W1800566695","https://openalex.org/W2010973140","https://openalex.org/W2056363353","https://openalex.org/W2105726509","https://openalex.org/W2110530236","https://openalex.org/W2112373045","https://openalex.org/W2119971988","https://openalex.org/W2124430127","https://openalex.org/W2134259640","https://openalex.org/W2137738542","https://openalex.org/W2139593284","https://openalex.org/W2148573461","https://openalex.org/W2153919611","https://openalex.org/W2155025530","https://openalex.org/W2161454533","https://openalex.org/W2163067248","https://openalex.org/W2165870048","https://openalex.org/W2166245380","https://openalex.org/W2167294808","https://openalex.org/W2168886131","https://openalex.org/W2169470509","https://openalex.org/W2170358724","https://openalex.org/W2406682001","https://openalex.org/W2804525671","https://openalex.org/W3016129726","https://openalex.org/W3102490463","https://openalex.org/W3216487844","https://openalex.org/W6605078041","https://openalex.org/W6633101944","https://openalex.org/W6636589124","https://openalex.org/W6637299323","https://openalex.org/W6713928540","https://openalex.org/W6785961528","https://openalex.org/W7008219709"],"related_works":["https://openalex.org/W4287880334","https://openalex.org/W2560215812","https://openalex.org/W4366700029","https://openalex.org/W2949601986","https://openalex.org/W4285230481","https://openalex.org/W4385769873","https://openalex.org/W4281634296","https://openalex.org/W2788972299","https://openalex.org/W4313645560","https://openalex.org/W2090412404"],"abstract_inverted_index":{"Characterizing":[0],"user":[1,27,75,114,126],"churn":[2,103],"has":[3,21],"become":[4],"an":[5,106],"important":[6],"topic":[7],"in":[8,13,66,85,167],"studying":[9],"P2P":[10],"networks,":[11],"both":[12],"theoretical":[14],"analysis":[15],"and":[16,38,41,77,105,127,144,164],"system":[17],"design.":[18],"Recent":[19],"work":[20],"shown":[22],"that":[23,45,58],"direct":[24,171],"sampling":[25,70,110,138,172],"of":[26,82,123,162],"lifetimes":[28,47,76],"may":[29],"lead":[30],"to":[31,149,170],"certain":[32],"bias":[33],"(arising":[34],"from":[35],"missed":[36],"peers":[37],"round-off":[39],"inconsistencies)":[40],"proposed":[42],"a":[43,79,99,146],"technique":[44,111],"estimates":[46],"based":[48],"on":[49],"sampled":[50],"residuals.":[51],"In":[52],"this":[53,94],"paper,":[54],"however,":[55],"we":[56],"show":[57],"under":[59],"non-stationary":[60,101],"arrivals,":[61],"which":[62,84],"are":[63],"often":[64],"present":[65],"real":[67],"systems,":[68],"residual-based":[69],"does":[71],"not":[72],"correctly":[73],"reconstruct":[74,150],"suffers":[78],"varying":[80],"degree":[81],"bias,":[83],"some":[86],"cases":[87],"makes":[88],"estimation":[89],"completely":[90],"impossible.":[91],"We":[92,155],"overcome":[93],"problem":[95],"using":[96],"two":[97],"contributions:":[98],"novel":[100,147],"ON/OFF":[102,121],"model":[104],"unbiased":[107],"randomized":[108],"residual":[109],"for":[112],"measuring":[113],"lifetimes.":[115],"The":[116,135],"former":[117],"allows":[118],"correlation":[119],"between":[120],"periods":[122],"the":[124,133,142,151,157],"same":[125],"exhibits":[128],"different":[129],"join":[130],"rates":[131],"during":[132,141],"day.":[134],"latter":[136],"spreads":[137],"points":[139],"uniformly":[140],"day":[143],"uses":[145],"estimator":[148],"underlying":[152],"lifetime":[153],"distribution.":[154],"finish":[156],"paper":[158],"with":[159],"experimental":[160],"measurements":[161],"Gnutella":[163],"discussing":[165],"reduction":[166],"overhead":[168],"compared":[169],"methods.":[173]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}