{"id":"https://openalex.org/W7164020014","doi":"https://doi.org/10.48550/arxiv.2606.07565","title":"STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms","display_name":"STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7164020014","doi":"https://doi.org/10.48550/arxiv.2606.07565"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07565","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,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07565","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138245005","display_name":"Ahmed Abdulaal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdulaal, Ahmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138211819","display_name":"Maruf Aytekin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aytekin, Maruf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138214089","display_name":"Thilaga kumaran Srinivasan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srinivasan, Thilaga kumaran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025477216","display_name":"Tomer Lancewicki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lancewicki, Tomer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T12127","display_name":"Software System Performance and Reliability","score":0.9416999816894531,"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":0.9416999816894531,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.0348999984562397,"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":0.005100000184029341,"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/cloud-computing","display_name":"Cloud computing","score":0.7544000148773193},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6643000245094299},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6456999778747559},{"id":"https://openalex.org/keywords/microservices","display_name":"Microservices","score":0.6352999806404114},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"Resource allocation","score":0.5426999926567078},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.5386000275611877},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46299999952316284},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/service","display_name":"Service (business)","score":0.42309999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8016999959945679},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7544000148773193},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6643000245094299},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C2778505942","wikidata":"https://www.wikidata.org/wiki/Q18344624","display_name":"Microservices","level":3,"score":0.6352999806404114},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.5426999926567078},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.5386000275611877},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44440001249313354},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3937000036239624},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35440000891685486},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31940001249313354},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C116537","wikidata":"https://www.wikidata.org/wiki/Q2169973","display_name":"Service provider","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C181889124","wikidata":"https://www.wikidata.org/wiki/Q380204","display_name":"Service level","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C191511416","wikidata":"https://www.wikidata.org/wiki/Q999278","display_name":"Customer satisfaction","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07565","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,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07565","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,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Intelligent":[0],"scaling":[1,34,124],"of":[2,55,141],"microservices":[3,157],"in":[4,59,90,107,148,168],"cloud":[5],"platforms":[6],"is":[7,152],"crucial":[8],"for":[9,70,154],"mitigating":[10],"escalating":[11],"compute":[12],"costs":[13],"while":[14,50],"avoiding":[15],"service":[16,127,175],"disruptions.":[17],"Current":[18],"solutions":[19,63,147],"are":[20,64],"limited":[21],"to":[22,32,122,166,170],"the":[23,39,52,91,109,139],"univariate":[24],"space,":[25],"typically":[26],"focusing":[27,46],"on":[28,47],"CPU":[29],"usage":[30],"alone":[31],"drive":[33],"decisions.":[35],"Moreover,":[36],"they":[37],"address":[38,75],"problem":[40],"as":[41],"a":[42,81],"purely":[43],"forecasting":[44],"task,":[45],"prediction":[48],"precision":[49],"neglecting":[51],"greater":[53],"risks":[54],"underestimation":[56],"and":[57,114,177],"delays":[58],"system":[60,100],"responsiveness.":[61],"Alternative":[62],"computationally":[65],"complex,":[66],"making":[67],"them":[68],"impractical":[69],"large-scale,":[71],"real-time":[72],"deployments.":[73],"To":[74],"these":[76],"challenges,":[77],"we":[78],"present":[79],"STARIXNet,":[80],"lightweight":[82],"neural":[83],"network":[84],"that":[85],"guides":[86],"resource":[87],"allocation":[88],"decisions":[89],"multivariate":[92],"space":[93],"by":[94,130,143],"capturing":[95],"spatio-temporal":[96],"relationships":[97],"among":[98],"multiple":[99,104],"metrics.":[101],"STARIXNet":[102,142,151],"models":[103],"quasi-dependent":[105],"attributes,":[106],"particular":[108],"(S)easonal,":[110],"(T)emporal,":[111],"(A)uto-(R)egressive":[112],"(I)ntegrated,":[113],"e(X)ogenous":[115],"patterns,":[116],"then":[117],"implements":[118],"an":[119],"aggregation":[120],"policy":[121],"finalize":[123],"decisions,":[125],"prioritizing":[126],"stability,":[128],"followed":[129],"cost-efficiency,":[131],"over":[132],"raw":[133],"forecast":[134],"accuracy.":[135],"We":[136],"empirically":[137],"demonstrate":[138],"performance":[140],"benchmarking":[144],"against":[145],"existing":[146],"real-world":[149],"settings.":[150],"deployed":[153],"critical":[155],"production":[156],"at":[158],"Walmart":[159],"achieving":[160],"tangible":[161],"savings":[162],"ranging":[163],"from":[164],"10\\%":[165],"50\\%,":[167],"addition":[169],"intangible":[171],"benefits":[172],"through":[173],"improved":[174],"stability":[176],"customer":[178],"experience.":[179]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}