{"id":"https://openalex.org/W7125009202","doi":"https://doi.org/10.1109/cloudcom67567.2025.11331409","title":"Hybrid Reactive Autoscaling for Task-Based Pipelines on Kubernetes","display_name":"Hybrid Reactive Autoscaling for Task-Based Pipelines on Kubernetes","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W7125009202","doi":"https://doi.org/10.1109/cloudcom67567.2025.11331409"},"language":null,"primary_location":{"id":"doi:10.1109/cloudcom67567.2025.11331409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudcom67567.2025.11331409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 lEEE International Conference on Cloud Computing Technology and Science (CloudCom)","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/A5006511439","display_name":"Andrey Nagiyev","orcid":null},"institutions":[{"id":"https://openalex.org/I129774422","display_name":"University of Vienna","ror":"https://ror.org/03prydq77","country_code":"AT","type":"education","lineage":["https://openalex.org/I129774422"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Andrey Nagiyev","raw_affiliation_strings":["University of Vienna,*Faculty of Computer Science,Vienna,Austria"],"affiliations":[{"raw_affiliation_string":"University of Vienna,*Faculty of Computer Science,Vienna,Austria","institution_ids":["https://openalex.org/I129774422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056654204","display_name":"Enes Bajrovi\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I129774422","display_name":"University of Vienna","ror":"https://ror.org/03prydq77","country_code":"AT","type":"education","lineage":["https://openalex.org/I129774422"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Enes Bajrovic","raw_affiliation_strings":["University of Vienna,*Faculty of Computer Science,Vienna,Austria"],"affiliations":[{"raw_affiliation_string":"University of Vienna,*Faculty of Computer Science,Vienna,Austria","institution_ids":["https://openalex.org/I129774422"]}]},{"author_position":"last","author":{"id":null,"display_name":"Siegfried Benkner","orcid":null},"institutions":[{"id":"https://openalex.org/I129774422","display_name":"University of Vienna","ror":"https://ror.org/03prydq77","country_code":"AT","type":"education","lineage":["https://openalex.org/I129774422"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Siegfried Benkner","raw_affiliation_strings":["University of Vienna,*Faculty of Computer Science,Vienna,Austria"],"affiliations":[{"raw_affiliation_string":"University of Vienna,*Faculty of Computer Science,Vienna,Austria","institution_ids":["https://openalex.org/I129774422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006511439"],"corresponding_institution_ids":["https://openalex.org/I129774422"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73720101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.5449000000953674,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.5449000000953674,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.05380000174045563,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.04619999974966049,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.6514999866485596},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5706999897956848},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5598000288009644},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5425999760627747},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.43639999628067017},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.42500001192092896},{"id":"https://openalex.org/keywords/frequency-scaling","display_name":"Frequency scaling","score":0.3815000057220459},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.3756999969482422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6983000040054321},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.6514999866485596},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5856000185012817},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.43639999628067017},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C157742956","wikidata":"https://www.wikidata.org/wiki/Q3237776","display_name":"Frequency scaling","level":3,"score":0.3815000057220459},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C160403385","wikidata":"https://www.wikidata.org/wiki/Q220543","display_name":"Queue","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C49154492","wikidata":"https://www.wikidata.org/wiki/Q5300","display_name":"Central processing unit","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.27309998869895935},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2653000056743622},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C141392084","wikidata":"https://www.wikidata.org/wiki/Q182661","display_name":"Excavator","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cloudcom67567.2025.11331409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudcom67567.2025.11331409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 lEEE International Conference on Cloud Computing Technology and Science (CloudCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6785243153572083}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2112193291","https://openalex.org/W2194775991","https://openalex.org/W2326090267","https://openalex.org/W2799047820","https://openalex.org/W2906810629","https://openalex.org/W2914180255","https://openalex.org/W3023238978","https://openalex.org/W3187688612","https://openalex.org/W3203256536","https://openalex.org/W4312439245","https://openalex.org/W4384521394","https://openalex.org/W4406265929"],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"Python-to-Kubernetes":[2],"(PTK),":[3],"a":[4,74,136],"hybrid":[5],"autoscaling":[6],"framework":[7,36],"for":[8,20],"pipeline-oriented,":[9],"task-based":[10],"Python":[11],"applications":[12],"on":[13],"Kubernetes.":[14],"PTK":[15,81],"coordinates":[16],"queue-length-driven":[17],"horizontal":[18,62],"scaling":[19,30,45],"CPU,":[21],"memory,":[22],"and":[23,33,47,57,63,96,100,121],"GPU,":[24],"together":[25],"with":[26,110,135],"reactive":[27],"in-place":[28],"vertical":[29,64],"of":[31],"CPU":[32,92],"memory.":[34],"The":[35],"introduces":[37],"source-code":[38],"annotations,":[39],"enabling":[40],"users":[41],"to":[42,60,107],"define":[43],"task-specific":[44],"constraints":[46],"automatically":[48],"generate":[49],"Kubernetes":[50],"manifests.":[51],"A":[52],"periodic":[53],"controller":[54],"uses":[55],"utilization":[56],"queue":[58],"metrics":[59],"coordinate":[61],"scaling,":[65],"improving":[66],"resource":[67],"efficiency":[68],"while":[69,86],"maintaining":[70],"pipeline":[71],"performance.":[72],"In":[73],"streaming":[75],"machine":[76],"learning":[77],"(ML)":[78],"inference":[79],"pipeline,":[80],"sustains":[82],"the":[83,102,116,122],"target":[84],"throughput":[85],"reducing":[87],"hourly":[88],"cost":[89,131],"by":[90,93,98,132],"40.6%,":[91],"32.1":[94],"%,":[95],"memory":[97],"22.4%,":[99],"lowering":[101],"GPU":[103],"count":[104],"from":[105],"4":[106],"3,":[108],"compared":[109,134],"an":[111],"uncoordinated":[112],"baseline":[113],"that":[114],"combines":[115],"Horizontal":[117],"Pod":[118,124],"Autoscaler":[119,125],"(HPA)":[120],"Vertical":[123],"(VPA).":[126],"It":[127],"also":[128],"cuts":[129],"peak":[130],"23.6%":[133],"queue-driven":[137],"HPA":[138],"baseline.":[139]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-01-21T00:00:00"}