{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:39:01Z","timestamp":1787063941431,"version":"3.56.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO\n                    <jats:sub>2<\/jats:sub>\n                    -aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO\n                    <jats:sub>2<\/jats:sub>\n                    decisions. The proposed algorithm is implemented using Kubernetes\u2019 scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios.\n                  <\/jats:p>","DOI":"10.1007\/s11227-023-05506-7","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T02:02:25Z","timestamp":1687831345000},"page":"549-569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Carbon emission-aware job scheduling for Kubernetes deployments"],"prefix":"10.1007","volume":"80","author":[{"given":"Tobias","family":"Piontek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kawsar","family":"Haghshenas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Aiello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"6481","key":"5506_CR1","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1126\/science.aba3758","volume":"367","author":"E Masanet","year":"2020","unstructured":"Masanet E, Shehabi A, Lei N, Smith S, Koomey J (2020) Recalibrating global data center energy-use estimates. Science 367(6481):984\u2013986","journal-title":"Science"},{"issue":"1","key":"5506_CR2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.3390\/challe6010117","volume":"6","author":"AS Andrae","year":"2015","unstructured":"Andrae AS, Edler T (2015) On global electricity usage of communication technology: trends to 2030. Challenges 6(1):117\u2013157","journal-title":"Challenges"},{"key":"5506_CR3","doi-asserted-by":"crossref","unstructured":"Fiorini L, Aiello M (2020) Predictive multi-objective scheduling with dynamic prices and marginal CO$$_2$$-emission intensities. In: Proceedings of the Eleventh ACM International Conference on Future Energy Systems pp. 196\u2013207","DOI":"10.1145\/3396851.3397732"},{"key":"5506_CR4","unstructured":"Qvist F (2020) Allowing everyone to use electricity at the right time for the climate. [Online]. Available: https:\/\/electricitymaps.com\/blog\/ifttt-blogpost\/"},{"key":"5506_CR5","unstructured":"James A, Schien D (2019) A low carbon kubernetes scheduler. In: ICT4S"},{"key":"5506_CR6","unstructured":"Various Authors. (2022) electricitymap\u2013the leading resource for 24\/7 electricity CO$$_2$$ data. [Online]. Available: https:\/\/electricitymaps.com\/"},{"key":"5506_CR7","first-page":"161","volume-title":"E-Energy \u201920: The Eleventh ACM International Conference on Future Energy Systems, Virtual Event, Australia, June 22-26, 2020","author":"L Fiorini","year":"2020","unstructured":"Fiorini L, Steg L, Aiello M (2020) Sustainability choices when cooking pasta. In: E-Energy \u201920: The Eleventh ACM International Conference on Future Energy Systems, Virtual Event, Australia, June 22-26, 2020. ACM, pp 161\u2013166"},{"issue":"1","key":"5506_CR8","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/JSYST.2014.2320800","volume":"9","author":"GA Pagani","year":"2015","unstructured":"Pagani GA, Aiello M (2015) Generating realistic dynamic prices and services for the smart grid. IEEE Syst J 9(1):191\u2013198","journal-title":"IEEE Syst J"},{"key":"5506_CR9","volume":"33","author":"S Rawas","year":"2022","unstructured":"Rawas S, Zekri A, El-Zaart A (2022) LECC: location, energy, carbon and cost-aware VM placement model in geo-distributed DCS. Sustain Comput Inf Syst 33:100649","journal-title":"Sustain Comput Inf Syst"},{"issue":"1","key":"5506_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TSC.2019.2919555","volume":"15","author":"K Haghshenas","year":"2019","unstructured":"Haghshenas K, Pahlevan A, Zapater M, Mohammadi S, Atienza D (2019) Magnetic: multi-agent machine learning-based approach for energy efficient dynamic consolidation in data centers. IEEE Trans Serv Comput 15(1):30\u201344","journal-title":"IEEE Trans Serv Comput"},{"issue":"5","key":"5506_CR11","doi-asserted-by":"publisher","first-page":"5677","DOI":"10.3233\/JIFS-189887","volume":"41","author":"T Renugadevi","year":"2021","unstructured":"Renugadevi T, Geetha K (2021) Task aware optimized energy cost and carbon emission-based virtual machine placement in sustainable data centers. J Intell Fuzzy Syst 41(5):5677\u20135689","journal-title":"J Intell Fuzzy Syst"},{"key":"5506_CR12","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.jpdc.2022.04.001","volume":"165","author":"D Zhao","year":"2022","unstructured":"Zhao D, Zhou J (2022) An energy and carbon-aware algorithm for renewable energy usage maximization in distributed cloud data centers. J Parallel and Distrib Comput 165:156\u2013166","journal-title":"J Parallel and Distrib Comput"},{"key":"5506_CR13","unstructured":"Radovanovic A, Koningstein R, Schneider I, Chen B, Duarte A, Roy B, Xiao D, Haridasan M, Hung P, Care N, et al (2021) Carbon-aware computing for datacenters. rXiv preprint arXiv:2106.11750"},{"key":"5506_CR14","doi-asserted-by":"crossref","unstructured":"Wiesner P, Behnke I, Scheinert D, Gontarska K, Thamsen L (2021) Let\u2019s wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud. arXiv preprint arXiv:2110.13234","DOI":"10.1145\/3464298.3493399"},{"key":"5506_CR15","unstructured":"Various Authors. Kubernetes. [Online]. Available: https:\/\/kubernetes.io\/"},{"key":"5506_CR16","unstructured":"Burns B, Beda J, Hightower K (2018) Kubernetes. Dpunkt"},{"key":"5506_CR17","unstructured":"SlashData (2021) The state of cloud native developement. [Online]. Available: https:\/\/www.cncf.io\/wp-content\/uploads\/2021\/12\/Q1-2021-State-of-Cloud-Native-development-FINAL.pdf"},{"key":"5506_CR18","unstructured":"Cloud Native Computing Foundation (2022) Annual survey 2021: the year kubernetes crossed the chasm. [Online]. Available: https:\/\/www.cncf.io\/wp-content\/uploads\/2022\/02\/CNCF-AR_FINAL-edits-15.2.21.pdf"},{"key":"5506_CR19","unstructured":"Various Authors (2021) Kubernetes\u2013cloud controller manager. [Online]. Available: https:\/\/kubernetes.io\/de\/docs\/concepts\/architecture\/cloud-controller\/"},{"key":"5506_CR20","unstructured":"Various Authors (2021) Kubernetes\u2013the burgeoning kubernetes scheduling system. [Online]. Available: https:\/\/kubernetes.io\/docs\/concepts\/scheduling-eviction\/pod-priority-preemption\/"},{"key":"5506_CR21","unstructured":"Various Authors (2021) Kubernetes\u2013scheduling framework. [Online]. Available: https:\/\/kubernetes.io\/docs\/reference\/command-line-tools-reference\/kube-scheduler\/"},{"key":"5506_CR22","unstructured":"Saenger CB Guinevere, TE (2020) Scheduler extender. [Online]. Available: https:\/\/github.com\/kubernetes\/design-proposals-archive\/blob\/main\/scheduling\/scheduler_extender.md"},{"key":"5506_CR23","unstructured":"Huang W (2020) Create a custom Kubernetes scheduler. [Online]. Available: https:\/\/developer.ibm.com\/articles\/creating-a-custom-kube-scheduler\/"},{"key":"5506_CR24","unstructured":"Huang W (2021) Scheduling framework. [Online]. Available: https:\/\/github.com\/kubernetes\/enhancements\/tree\/master\/keps\/sig-scheduling\/624-scheduling-framework"},{"issue":"2","key":"5506_CR25","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1109\/TSG.2014.2363583","volume":"6","author":"J Li","year":"2014","unstructured":"Li J, Bao Z, Li Z (2014) Modeling demand response capability by internet data centers processing batch computing jobs. IEEE Trans Smart Grid 6(2):737\u2013747","journal-title":"IEEE Trans Smart Grid"},{"key":"5506_CR26","unstructured":"Haghshenas K, Taheri S, Goudarzi M, Mohammadi S (2020) Infrastructure aware heterogeneous-workloads scheduling for data center energy cost minimization. IEEE Trans Cloud Comput"},{"key":"5506_CR27","unstructured":"Omura S (2022) k8s-scheduler-extender-example. [Online]. Available: https:\/\/github.com\/everpeace\/k8s-scheduler-extender-example"},{"key":"5506_CR28","first-page":"1","volume-title":"Conference on New Media Studies (CoNMedia)","author":"S Hansun","year":"2013","unstructured":"Hansun S (2013) A new approach of moving average method in time series analysis. In: Conference on New Media Studies (CoNMedia). IEEE, pp 1\u20134"},{"issue":"1","key":"5506_CR29","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s00591-010-0080-8","volume":"58","author":"F Klinker","year":"2011","unstructured":"Klinker F (2011) Exponential moving average versus moving exponential average. Math Semesterber 58(1):97\u2013107","journal-title":"Math Semesterber"},{"key":"5506_CR30","unstructured":"Dash S (2012) A comparative study of moving averages: simple, weighted, and exponential. Appl Tech Anal"},{"key":"5506_CR31","unstructured":"Fan) W Q L, Kai Z (2021) The burgeoning kubernetes scheduling system. [Online]. Available: https:\/\/www.alibabacloud.com\/blog\/the-burgeoning-kubernetes-scheduling-system-part-1-scheduling-framework_597318"},{"key":"5506_CR32","unstructured":"Various Authors. Kubestone\u2013the grid workloads archive. [Online]. Available: http:\/\/gwa.ewi.tudelft.nl\/"},{"issue":"2","key":"5506_CR33","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/1273440.1250665","volume":"35","author":"X Fan","year":"2007","unstructured":"Fan X, Weber W-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput Archit News 35(2):13\u201323","journal-title":"ACM SIGARCH Comput Archit News"},{"issue":"6","key":"5506_CR34","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1109\/TSC.2019.2894742","volume":"14","author":"W Wu","year":"2019","unstructured":"Wu W, Wang W, Fang X, Luo J, Vasilakos AV (2019) Electricity price-aware consolidation algorithms for time-sensitive VM services in cloud systems. IEEE Trans Serv Comput 14(6):1726\u20131738","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"5506_CR35","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1109\/TCST.2017.2783366","volume":"27","author":"T Van Damme","year":"2018","unstructured":"Van Damme T, De Persis C, Tesi P (2018) Optimized thermal-aware job scheduling and control of data centers. IEEE Trans Control Syst Technol 27(2):760\u2013771","journal-title":"IEEE Trans Control Syst Technol"},{"issue":"2","key":"5506_CR36","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TC.2015.2428695","volume":"65","author":"D Cheng","year":"2015","unstructured":"Cheng D, Rao J, Jiang C, Zhou X (2015) Elastic power-aware resource provisioning of heterogeneous workloads in self-sustainable datacenters. IEEE Trans Comput 65(2):508\u2013521","journal-title":"IEEE Trans Comput"},{"issue":"4","key":"5506_CR37","first-page":"1119","volume":"5","author":"SR Jena","year":"2014","unstructured":"Jena SR, Padhy S, Ima B (2014) Minimizing CO$$_2$$ emissions on cloud data centers. Int J Sci Eng Res 5(4):1119\u20131122","journal-title":"Int J Sci Eng Res"},{"key":"5506_CR38","doi-asserted-by":"crossref","unstructured":"Aksanli B, Venkatesh J, Zhang L, Rosing T (2011) Utilizing green energy prediction to schedule mixed batch and service jobs in data centers. In: Proceedings of the 4th workshop on power-aware computing and systems, pp 1\u20135","DOI":"10.1145\/2039252.2039257"},{"key":"5506_CR39","doi-asserted-by":"crossref","unstructured":"Lin W-T, Chen G, Li H (2022) Carbon-aware load balance control of data centers with renewable generations. IEEE Trans Cloud Comput","DOI":"10.1109\/TCC.2022.3150391"},{"key":"5506_CR40","unstructured":"Narayanan D, Santhanam K, Kazhamiaka F, Phanishayee A, Zaharia M (2020) Analysis and exploitation of dynamic pricing in the public cloud for ml training. In: VLDB DISPA Workshop 2020"},{"key":"5506_CR41","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1109\/ICPSAsia55496.2022.9949832","volume-title":"IEEE\/IAS industrial and commercial power system Asia (I &CPS Asia)","author":"P Wang","year":"2022","unstructured":"Wang P, Liu W, Cheng M, Ding Z, Wang Y (2022) Electricity and carbon-aware task scheduling in geo-distributed internet data centers. In: IEEE\/IAS industrial and commercial power system Asia (I &CPS Asia). IEEE, pp 1416\u20131421"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05506-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05506-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05506-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T06:25:29Z","timestamp":1704695129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05506-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["5506"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05506-7","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"14 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}