GreenK8s: Green-aware Scheduling for Sustainable Kubernetes Cluster Management
With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities t...
Uloženo v:
| Vydáno v: | Proceedings / IEEE International Conference on Cluster Computing s. 1 - 12 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
02.09.2025
|
| Témata: | |
| ISSN: | 2168-9253 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities to promote green, low-carbon operations. This paper presents a Green-Aware Scheduling Framework for Kubernetes, named GreenK8s, aimed at minimizing the use of brown energy and maximizing the utilization of renewable energy sources, specifically solar power. Our framework integrates real-time power consumption monitoring with predictive solar energy models to intelligently schedule workloads based on energy availability. The proposed solution incorporates an AI-based solar power prediction model, Pod oversubscription strategies, and a novel scheduler, enabling Kubernetes to dynamically adapt to both the type and availability of green energy. Extensive experiments using the real-world Google Borg dataset and a realistic Kubernetes testbed demonstrate that GreenK8s reduces total energy consumption by up to 39 % and increases the average share of green energy in total consumption to 50.65 %, compared to state-of-the-art baselines. This work provides a promising approach to improve operational efficiency and sustainability in data centers. |
|---|---|
| AbstractList | With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in cloud computing environments. However, current schedulers focus solely on performance, lacking awareness of energy types and opportunities to promote green, low-carbon operations. This paper presents a Green-Aware Scheduling Framework for Kubernetes, named GreenK8s, aimed at minimizing the use of brown energy and maximizing the utilization of renewable energy sources, specifically solar power. Our framework integrates real-time power consumption monitoring with predictive solar energy models to intelligently schedule workloads based on energy availability. The proposed solution incorporates an AI-based solar power prediction model, Pod oversubscription strategies, and a novel scheduler, enabling Kubernetes to dynamically adapt to both the type and availability of green energy. Extensive experiments using the real-world Google Borg dataset and a realistic Kubernetes testbed demonstrate that GreenK8s reduces total energy consumption by up to 39 % and increases the average share of green energy in total consumption to 50.65 %, compared to state-of-the-art baselines. This work provides a promising approach to improve operational efficiency and sustainability in data centers. |
| Author | Xu, Minxian Toosi, Adel N. Sun, Yifan |
| Author_xml | – sequence: 1 givenname: Yifan surname: Sun fullname: Sun, Yifan email: yifan.sun.4@student.unimelb.edu.au organization: The University of Melbourne,Computing and Information Systems,Melbourne,Australia – sequence: 2 givenname: Minxian surname: Xu fullname: Xu, Minxian email: mx.xu@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,China – sequence: 3 givenname: Adel N. surname: Toosi fullname: Toosi, Adel N. email: adel.toosi@unimelb.edu.au organization: The University of Melbourne,Computing and Information Systems,Melbourne,Australia |
| BookMark | eNo1j0tLAzEURqMoWGv_gYvgfmqSO3lcdzLUKq0Ktq5LMr2pI9NUMlPEf2_xsTofZ_HBOWcnaZeIsSspxlIKvK7mr4vl5EUjlGqshNIHLZ0pNR6xEVp0AFKDkOiO2UBJ4wpUGs7Yede9CwEWhBmwp2kmSjPX3fCfVfhPn4kv6jda79smbXjcZb7Yd71vkg8t8dk-UE7UU8er9uAp80ef_Ia2lPoLdhp929Hoj0O2vJssq_ti_jx9qG7nRYPQF2AjQjQiYBBexKClAaqFL-PaYl2SRqFCaWodrRWg0VG0ikwoXS1JagdDdvl72xDR6iM3W5-_Vv_58A0Tw1JU |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/CLUSTER59342.2025.11186459 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings Accès UT - IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9798331530198 |
| EISSN | 2168-9253 |
| EndPage | 12 |
| ExternalDocumentID | 11186459 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Guangdong Basic and Applied Basic Research Foundation grantid: 2024A1515010251,2023B1515130002 funderid: 10.13039/501100021171 |
| GroupedDBID | 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL RNS |
| ID | FETCH-LOGICAL-i93t-37f93f60b9b0a0fb5163ec0a4fd79c4e5902b46c5f7703598ef72e6b48c1e1583 |
| IEDL.DBID | RIE |
| IngestDate | Wed Oct 15 14:21:20 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-37f93f60b9b0a0fb5163ec0a4fd79c4e5902b46c5f7703598ef72e6b48c1e1583 |
| PageCount | 12 |
| ParticipantIDs | ieee_primary_11186459 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Sept.-2 |
| PublicationDateYYYYMMDD | 2025-09-02 |
| PublicationDate_xml | – month: 09 year: 2025 text: 2025-Sept.-2 day: 02 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings / IEEE International Conference on Cluster Computing |
| PublicationTitleAbbrev | CLUSTER |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0037306 |
| Score | 2.3019288 |
| Snippet | With the rise of large-scale data centers and increasing demand for energy-efficient operations, there is a growing need to optimize the use of green energy in... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Adaptation models Cloud computing Cluster architectures Data centers Energy efficiency Energy efficient Green energy Green products Kubernetes Predictive models Processor scheduling Scheduling Solar energy Sustainable development |
| Title | GreenK8s: Green-aware Scheduling for Sustainable Kubernetes Cluster Management |
| URI | https://ieeexplore.ieee.org/document/11186459 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pa8MgFJa17LBT96Njv_Gwa9okGo27lpVBSym0g95KnnmywkhHmmz__tQmKzvssJsogqjP96nv-x4hj0msImAiDCRqe0GxgDwA0CyIcw3c4g0QxuvMTuVslq5Wat6Q1T0XBhF98BkOXNH_5edbXbunsqG1y9SJn3RIR0qxJ2u1xy6zW1U0qqJRqIaj6evCAsJEMe74VnEyaHv_yqPi3ci4988BnJL-gZBH5z-u5owcYXFOem1GBtoY6AWZ-TCaSbp7or4UZF9Zibb9zXoURzynFqPSxYE0RSc1YFm411c6eq-dagI9RMT0yXL8vBy9BE3GhGCjWGUPC6OYESEoCLPQQGLBFuow4yaXSnN0Ui3AhU6MlF66D42MUQBPdYRRkrJL0i22BV4RKjDOc2UgSzlya_TAmEGZ81TJTEsO16Tvpmf9sdfEWLczc_NH_S05cYvgo7PiO9KtyhrvybH-rDa78sGv5Dco7Z-Z |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH7oFPQ0f0z8bQ5eu7VN2jReh2OyWgabsNto0hcUpJNu1X_fJOscHjx4CwmBkOTlfUne9z2A-ygUgaSx73FU5oJiALknpaJeWCjJDN6QsXY6synPsmQ2E-OGrO64MIjogs-wa4vuL79YqNo-lfWMXSZW_GQX9iLGQn9N19ocvNRs1rjRFQ180eunLxMDCSNBmWVchVF30_9XJhXnSAbtfw7hCDpbSh4Z_zibY9jB8gTam5wMpDHRU8hcIM0oWT4QV_Lyr7xC0_5qfIqlnhODUslkS5sio1piVdr3V9J_r61uAtnGxHRgOnic9odekzPBexN0ZY4LLaiOfSmkn_taRgZuofJzpgsuFEMr1iJZrCLNuRPvQ81DjCVLVIBBlNAzaJWLEs-BxBgWhdAyTxgyY_aSUo28YIngueJMXkDHTs_8Y62KMd_MzOUf9XdwMJw-p_P0KRtdwaFdEBerFV5Da1XVeAP76nP1tqxu3ap-A6TlouA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+%2F+IEEE+International+Conference+on+Cluster+Computing&rft.atitle=GreenK8s%3A+Green-aware+Scheduling+for+Sustainable+Kubernetes+Cluster+Management&rft.au=Sun%2C+Yifan&rft.au=Xu%2C+Minxian&rft.au=Toosi%2C+Adel+N.&rft.date=2025-09-02&rft.pub=IEEE&rft.eissn=2168-9253&rft.spage=1&rft.epage=12&rft_id=info:doi/10.1109%2FCLUSTER59342.2025.11186459&rft.externalDocID=11186459 |