{"id":"https://openalex.org/W4413155243","doi":"https://doi.org/10.1109/access.2025.3598712","title":"Hierarchical Reinforcement Learning for Energy-Efficient API Traffic Optimization in Large-Scale Advertising Systems","display_name":"Hierarchical Reinforcement Learning for Energy-Efficient API Traffic Optimization in Large-Scale Advertising Systems","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413155243","doi":"https://doi.org/10.1109/access.2025.3598712"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3598712","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598712","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3598712","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035309564","display_name":"Enkai Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Enkai Ji","raw_affiliation_strings":["Department of Computer Science, Rutgers University, New Brunswick, NJ, USA","Rutgers University, New Brunswick, NJ, USA"],"raw_orcid":"https://orcid.org/0009-0005-8645-4736","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]},{"raw_affiliation_string":"Rutgers University, New Brunswick, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yihan Wang","orcid":"https://orcid.org/0009-0002-1781-5233"},"institutions":[{"id":"https://openalex.org/I110357561","display_name":"University of the Sciences","ror":"https://ror.org/048gmay44","country_code":"US","type":"education","lineage":["https://openalex.org/I110357561"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]},{"id":"https://openalex.org/I922845939","display_name":"Philadelphia University","ror":"https://ror.org/03zzmyz63","country_code":"US","type":"education","lineage":["https://openalex.org/I922845939"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihan Wang","raw_affiliation_strings":["School of Engineering and Applied Science, The University of Pennsylvania, Philadelphia, PA, USA","University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0009-0002-1781-5233","affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Science, The University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I110357561","https://openalex.org/I79576946","https://openalex.org/I922845939"]},{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118604857","display_name":"Suchuan Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suchuan Xing","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA","Duke University, Durham, NC, USA"],"raw_orcid":"https://orcid.org/0009-0005-5213-4371","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jianian Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianian Jin","raw_affiliation_strings":["Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":13.0459,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.98919476,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"142493","last_page":"142516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11478","display_name":"Caching and Content Delivery","score":0.963100016117096,"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/T11478","display_name":"Caching and Content Delivery","score":0.963100016117096,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9621999859809875,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12720","display_name":"Multimedia Communication and Technology","score":0.9510999917984009,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8075355291366577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7873385548591614},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5077387094497681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3137703835964203}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8075355291366577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7873385548591614},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5077387094497681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3137703835964203},{"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":2,"locations":[{"id":"doi:10.1109/access.2025.3598712","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598712","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b14c6fbd1e354ea3b801992887fd9df5","is_oa":true,"landing_page_url":"https://doaj.org/article/b14c6fbd1e354ea3b801992887fd9df5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 142493-142516 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3598712","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3598712","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1985759455","https://openalex.org/W1993627350","https://openalex.org/W2021375049","https://openalex.org/W2033798573","https://openalex.org/W2075233755","https://openalex.org/W2076618162","https://openalex.org/W2084089567","https://openalex.org/W2102558581","https://openalex.org/W2145339207","https://openalex.org/W2277814926","https://openalex.org/W2290203315","https://openalex.org/W2464603716","https://openalex.org/W2475334473","https://openalex.org/W2546571074","https://openalex.org/W2607601895","https://openalex.org/W2723293840","https://openalex.org/W2746385174","https://openalex.org/W2764100055","https://openalex.org/W2766447205","https://openalex.org/W2767988112","https://openalex.org/W2768254111","https://openalex.org/W2790686993","https://openalex.org/W2795411118","https://openalex.org/W2799310732","https://openalex.org/W2819252821","https://openalex.org/W2940478829","https://openalex.org/W2951002685","https://openalex.org/W2952551358","https://openalex.org/W2953198391","https://openalex.org/W2963026732","https://openalex.org/W2963549123","https://openalex.org/W2964227312","https://openalex.org/W2968986602","https://openalex.org/W2970125901","https://openalex.org/W2970148517","https://openalex.org/W2991046523","https://openalex.org/W3023238978","https://openalex.org/W3026089842","https://openalex.org/W3100789280","https://openalex.org/W3121342653","https://openalex.org/W3140913729","https://openalex.org/W3168892396","https://openalex.org/W4385245566","https://openalex.org/W4392405664","https://openalex.org/W4399529624","https://openalex.org/W4404522037","https://openalex.org/W4409106566","https://openalex.org/W6628927740","https://openalex.org/W6684921986","https://openalex.org/W6687063787","https://openalex.org/W6687681856","https://openalex.org/W6738796088","https://openalex.org/W6739193204","https://openalex.org/W6747473740","https://openalex.org/W6748839928","https://openalex.org/W6752089545","https://openalex.org/W6754447547","https://openalex.org/W6766805167","https://openalex.org/W6776438516"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4306904969","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2138720691","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Digital":[0],"advertising":[1,38,136],"infrastructure":[2,191],"represents":[3],"a":[4,54,78,85,101,201],"substantial":[5],"component":[6],"of":[7,104],"global":[8,112],"computing":[9,198,216],"resources,":[10],"with":[11,84,95,219],"significant":[12,146],"environmental":[13,72],"impact":[14],"due":[15],"to":[16,45,66,91,166,196],"its":[17],"massive":[18],"energy":[19,49,116,163,209],"consumption":[20,164,226],"and":[21,48,70,158,178,211,227],"carbon":[22],"footprint.":[23],"This":[24,193],"work":[25,194],"addresses":[26],"the":[27],"sustainability":[28,147],"challenges":[29],"posed":[30],"by":[31,199],"inefficient":[32],"API":[33,63],"traffic":[34,64,120,176],"management":[35],"in":[36,151,156,161],"large-scale":[37],"systems,":[39,217],"where":[40],"conventional":[41],"static":[42],"approaches":[43],"lead":[44],"resource":[46,68,96,126,206],"overprovisioning":[47],"waste.":[50],"We":[51],"propose":[52],"AdaptiveGate,":[53],"sustainability-oriented":[55],"hierarchical":[56],"reinforcement":[57],"learning":[58],"framework":[59,99,202],"that":[60,184,203],"dynamically":[61],"optimizes":[62,204],"flows":[65],"enhance":[67],"efficiency":[69],"reduce":[71],"impact.":[73],"The":[74,169],"proposed":[75],"methodology":[76],"employs":[77],"constrained":[79],"Markov":[80],"Decision":[81],"Process":[82],"formulation":[83],"multi-objective":[86],"reward":[87],"function":[88],"explicitly":[89],"designed":[90],"balance":[92],"system":[93,170],"performance":[94],"efficiency.":[97],"Our":[98],"implements":[100],"two-tier":[102],"architecture":[103],"twin":[105],"delayed":[106],"Deep":[107],"Deterministic":[108],"Policy":[109],"Gradient":[110],"agents:":[111],"agents":[113,124],"minimize":[114],"cross-datacenter":[115],"expenditure":[117],"through":[118,128],"intelligent":[119],"routing,":[121],"while":[122],"local":[123],"maximize":[125],"utilization":[127],"service-specific":[129],"load":[130],"balancing.":[131],"Empirical":[132],"evaluation":[133],"on":[134],"production":[135],"systems":[137],"processing":[138],"over":[139],"2.5":[140],"million":[141],"requests":[142],"per":[143],"second":[144],"reveals":[145],"improvements:":[148],"42.3%":[149],"reduction":[150],"tail":[152],"latency,":[153],"35.7%":[154],"increase":[155],"throughput,":[157],"18%":[159],"decrease":[160],"overall":[162],"compared":[165],"state-of-the-art":[167],"methods.":[168],"demonstrates":[171],"exceptional":[172],"adaptability":[173],"across":[174],"diverse":[175],"conditions":[177],"operational":[179],"scales,":[180],"providing":[181],"compelling":[182],"evidence":[183],"AI-driven":[185],"methods":[186],"can":[187],"substantially":[188],"improve":[189],"digital":[190],"sustainability.":[192],"contributes":[195],"sustainable":[197],"establishing":[200],"computational":[205],"allocation,":[207],"minimizes":[208],"waste,":[210],"advances":[212],"environmentally":[213],"responsible":[214,225],"high-performance":[215],"aligning":[218],"multiple":[220],"Sustainable":[221],"Development":[222],"Goals":[223],"including":[224],"affordable":[228],"clean":[229],"energy.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":13}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
