{"id":"https://openalex.org/W4416620237","doi":"https://doi.org/10.48550/arxiv.2510.20495","title":"Accurate Performance Predictors for Edge Computing Applications","display_name":"Accurate Performance Predictors for Edge Computing Applications","publication_year":2025,"publication_date":"2025-10-23","ids":{"openalex":"https://openalex.org/W4416620237","doi":"https://doi.org/10.48550/arxiv.2510.20495"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.20495","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.20495","pdf_url":"https://arxiv.org/pdf/2510.20495","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.20495","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042296569","display_name":"Panagiotis Giannakopoulos","orcid":"https://orcid.org/0009-0005-2726-6786","observed_orcids":["https://orcid.org/0009-0005-2726-6786"]},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Giannakopoulos, Panagiotis","raw_affiliation_strings":["Eindhoven University of Technology Eindhoven , The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology Eindhoven , The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057428135","display_name":"Bart van Knippenberg","orcid":"https://orcid.org/0000-0002-6859-332X","observed_orcids":["https://orcid.org/0000-0002-6859-332X"]},"institutions":[{"id":"https://openalex.org/I4210102558","display_name":"Thermo Fisher Scientific (Netherlands)","ror":"https://ror.org/01139ec29","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210102558","https://openalex.org/I4210142480"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"van Knippenberg, Bart","raw_affiliation_strings":["Thermo Fisher Scientific Eindhoven , The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thermo Fisher Scientific Eindhoven , The Netherlands","institution_ids":["https://openalex.org/I4210102558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040842871","display_name":"Kishor Chandra Joshi","orcid":"https://orcid.org/0000-0002-6047-5812","observed_orcids":["https://orcid.org/0000-0002-6047-5812"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joshi, Kishor Chandra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080891126","display_name":"Nicola Calabretta","orcid":"https://orcid.org/0000-0001-9162-4983","observed_orcids":["https://orcid.org/0000-0001-9162-4983"]},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Calabretta, Nicola","raw_affiliation_strings":["Eindhoven University of Technology Eindhoven , The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology Eindhoven , The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091641200","display_name":"Georgios Exarchakos","orcid":"https://orcid.org/0000-0002-5773-0720","observed_orcids":["https://orcid.org/0000-0002-5773-0720"]},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Exarchakos, George","raw_affiliation_strings":["Eindhoven University of Technology Eindhoven , The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology Eindhoven , The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.4189000129699707,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.4189000129699707,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.31690001487731934,"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/T12127","display_name":"Software System Performance and Reliability","score":0.1160999983549118,"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/performance-prediction","display_name":"performance prediction","score":0.8309999704360962},{"id":"https://openalex.org/keywords/edge-computing","display_name":"edge computing","score":0.9959999918937683},{"id":"https://openalex.org/keywords/application-scheduling","display_name":"application scheduling","score":0.5429999828338623},{"id":"https://openalex.org/keywords/resource-management","display_name":"resource management","score":0.890999972820282},{"id":"https://openalex.org/keywords/node-heterogeneity","display_name":"node heterogeneity","score":0.8899999856948853},{"id":"https://openalex.org/keywords/inference-latency","display_name":"inference latency","score":0.7990000247955322},{"id":"https://openalex.org/keywords/monitoring-indicators","display_name":"monitoring indicators","score":0.7590000033378601},{"id":"https://openalex.org/keywords/real-time-applications","display_name":"real-time applications","score":0.8209999799728394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7953000068664551},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7333999872207642},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5637000203132629},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5098000168800354},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.49549999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4499000012874603},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.43470001220703125},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.39649999141693115},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.39309999346733093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3779999911785126},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3395000100135803},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C107568181","wikidata":"https://www.wikidata.org/wiki/Q5319000","display_name":"Dynamic priority scheduling","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.20495","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.20495","pdf_url":"https://arxiv.org/pdf/2510.20495","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.20495","doi":"https://doi.org/10.48550/arxiv.2510.20495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.20495","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.20495","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.20495","pdf_url":"https://arxiv.org/pdf/2510.20495","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6460498982","display_name":null,"funder_award_id":"18651","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321651","display_name":"Technische Universiteit Eindhoven","ror":"https://ror.org/02c2kyt77"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416620237.pdf","grobid_xml":"https://content.openalex.org/works/W4416620237.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W1924770834"],"related_works":["https://openalex.org/W7133339582","https://openalex.org/W4408092574","https://openalex.org/W7139989774","https://openalex.org/W7104183076","https://openalex.org/W3130644969","https://openalex.org/W7007993012","https://openalex.org/W4409188348","https://openalex.org/W4415250844","https://openalex.org/W6891905456","https://openalex.org/W4408044501"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2,32,80,84,97],"application":[3,104],"performance":[4,22,52,105],"is":[5],"critical":[6],"for":[7,137],"enabling":[8],"effective":[9],"scheduling":[10],"and":[11,35,49,59,106,128,149,166],"resource":[12,130,164],"management":[13],"in":[14,23,111,152,168],"resource-constrained":[15],"dynamic":[16,112,153],"edge":[17,160],"environments.":[18],"However,":[19],"achieving":[20],"predictable":[21,169],"such":[24,157],"environments":[25,161],"remains":[26],"challenging":[27],"due":[28],"to":[29,62,72,103],"the":[30,36,64,85,94,98,135],"co-location":[31,113,154],"multiple":[33,109],"applications":[34],"node":[37],"heterogeneity.":[38],"To":[39],"address":[40],"this,":[41],"we":[42,117],"propose":[43],"a":[44,138],"methodology":[45,140],"that":[46,141],"automatically":[47],"builds":[48],"assesses":[50],"various":[51],"predictors.":[53],"This":[54],"approach":[55],"prioritizes":[56],"both":[57],"accuracy":[58,74,148],"inference":[60,78,150],"time":[61,79],"identify":[63],"most":[65,99],"efficient":[66],"model.":[67],"Our":[68,132],"predictors":[69,90,144,158],"achieve":[70],"up":[71],"90%":[73],"while":[75],"maintaining":[76],"an":[77],"less":[81],"than":[82],"1%":[83],"Round":[86],"Trip":[87],"Time.":[88],"These":[89],"are":[91],"trained":[92],"on":[93],"historical":[95],"state":[96],"correlated":[100],"monitoring":[101],"metrics":[102],"evaluated":[107],"across":[108],"servers":[110],"scenarios.":[114,155],"As":[115],"usecase":[116],"consider":[118],"electron":[119],"microscopy":[120],"(EM)":[121],"workflows,":[122],"which":[123],"have":[124],"stringent":[125],"real-time":[126],"demands":[127],"diverse":[129],"requirements.":[131],"findings":[133],"emphasize":[134],"need":[136],"systematic":[139],"selects":[142],"server-specific":[143],"by":[145],"jointly":[146],"optimizing":[147],"latency":[151],"Integrating":[156],"into":[159],"can":[162],"improve":[163],"utilization":[165],"result":[167],"performance.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2025-10-25T00:00:00"}