{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T11:04:47Z","timestamp":1766055887401,"version":"3.48.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"DOI":"10.1186\/s13677-025-00815-z","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T18:23:58Z","timestamp":1765477438000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SpectraNet: a lightweight hybrid time\u2013frequency deep learning framework for sustainable cloud workload forecasting"],"prefix":"10.1186","volume":"14","author":[{"given":"Nahin F.","family":"Siddiqui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zarif Safwan","family":"Hoque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Ehsanul","family":"Haque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9529-5208","authenticated-orcid":false,"given":"Abu Raihan Mostofa","family":"Kamal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-2214","authenticated-orcid":false,"given":"A. K. M.","family":"Azad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-9399","authenticated-orcid":false,"given":"Salem A.","family":"Alyami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4315-5243","authenticated-orcid":false,"given":"Md Azam","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"815_CR1","doi-asserted-by":"crossref","unstructured":"Rossi A, Visentin A, Prestwich S, Brown KN (2022) Bayesian uncertainty modelling for cloud workload prediction. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD), IEEE, Barcelona, Spain, 19\u201329","DOI":"10.1109\/CLOUD55607.2022.00018"},{"issue":"2","key":"815_CR2","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1109\/TCC.2022.3146615","volume":"11","author":"V Cozzolino","year":"2022","unstructured":"Cozzolino V, Tonetto L, Mohan N, Ding AY, Ott J (2022) Nimbus: towards latency-energy efficient task offloading for ar services. IEEE Trans on Cloud Comput 11(2):1530\u20131545","journal-title":"IEEE Trans On Cloud Comput"},{"issue":"4","key":"815_CR3","first-page":"5714","volume":"17","author":"J Kumar","year":"2023","unstructured":"Kumar J, Saxena D, Singh AK, Vasilakos (2023) A.V.: a quantum controlled-not neural network-based load forecast and management model for smart grid. IEEE Syst J 17(4):5714\u20135725","journal-title":"IEEE Syst J"},{"key":"815_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.jss.2019.05.031","volume":"155","author":"M Xu","year":"2019","unstructured":"Xu M, Buyya R (2019) Brownoutcon: a software system based on brownout and containers for energy-efficient cloud computing. J Syst And Softw 155:91\u2013103","journal-title":"J Syst And Softw"},{"issue":"10","key":"815_CR5","doi-asserted-by":"publisher","first-page":"6256","DOI":"10.3390\/su14106256","volume":"14","author":"S Bharany","year":"2022","unstructured":"Bharany S, Sharma S, Khalaf OI, Abdulsahib GM, Al Humaimeedy AS, Aldhyani TH, Maashi M, Alkahtani (2022) H.: a systematic survey on energyefficient techniques in sustainable cloud computing. Sustainability 14(10):6256","journal-title":"Sustainability"},{"key":"815_CR6","doi-asserted-by":"crossref","unstructured":"Saxena D, Singh (2024) A.K.: a comprehensive survey on sustainable resource management in cloud computing environments. Authorea Preprints","DOI":"10.36227\/techrxiv.170473729.96561302\/v1"},{"issue":"1","key":"815_CR7","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1002\/spe.995","volume":"41","author":"RN Calheiros","year":"2011","unstructured":"Calheiros RN, Ranjan R, Beloglazov A, Rose CAFD, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw: Pract And Exper 41(1):23\u201350. https:\/\/doi.org\/10.1002\/spe.995","journal-title":"Softw: Pract And Exper"},{"key":"815_CR8","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1007\/s11227-010-0421-3","volume":"60","author":"YC Lee","year":"2012","unstructured":"Lee YC, Zomaya AY (2012) Energy efficient utilization of resources in cloud computing systems. The J Supercomput 60:268\u2013280","journal-title":"The J Supercomput"},{"issue":"3","key":"815_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s00607-025-01435-w","volume":"107","author":"H Wang","year":"2025","unstructured":"Wang H, Mathews KJ, Golec M, Gill SS, Uhlig S (2025) Amazonaicloud: proactive resource allocation using amazon chronos based time series model for sustainable cloud computing. Computing 107(3):77","journal-title":"Computing"},{"key":"815_CR10","doi-asserted-by":"crossref","unstructured":"Sarikaa S, Niranjana S, Sri Vishnu Deepika K (2021) Time series forecasting of cloud resource usage. In 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), IEEE, Arad, Romania, 372\u2013382","DOI":"10.1109\/ICCCA52192.2021.9666444"},{"key":"815_CR11","doi-asserted-by":"publisher","unstructured":"Pomeroy J (2024) Transforming business intelligence: leveraging generative AI and Predictive analytics in cloud environments. https:\/\/doi.org\/10.13140\/RG.2.2.34053.46560","DOI":"10.13140\/RG.2.2.34053.46560"},{"issue":"7","key":"815_CR12","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1111\/j.1553-2712.1998.tb02493.x","volume":"5","author":"BK Nelson","year":"1998","unstructured":"Nelson BK (1998) Time series analysis using autoregressive integrated moving average (arima) models. Academic Emerg Med 5(7):739\u2013744","journal-title":"Academic Emerg Med"},{"issue":"4","key":"815_CR13","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1007\/s10586-019-03010-3","volume":"23","author":"M Masdari","year":"2020","unstructured":"Masdari M, Khoshnevis A (2020) A survey and classification of the workload forecasting methods in cloud computing. Cluster Comput 23(4):2399\u20132424","journal-title":"Cluster Comput"},{"key":"815_CR14","doi-asserted-by":"crossref","unstructured":"Sahay MS, Ganta S, Vardhan BNV, Srilakshmi K (2024) A comprehensive study on improving time series forecasting precision. Semin Med Writ And Educ 3 7","DOI":"10.56294\/mw2024.588"},{"issue":"11","key":"815_CR15","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/info14110598","volume":"14","author":"A Casolaro","year":"2023","unstructured":"Casolaro A, Capone V, Iannuzzo G, Camastra F (2023) Deep learning for time series forecasting: advances and open problems. Information 14(11):598","journal-title":"Information"},{"issue":"7","key":"815_CR16","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.3390\/electronics14071303","volume":"14","author":"Z Yang","year":"2025","unstructured":"Yang Z, Yin M, Liao J, Xie F, Zheng P, Li J, Hua B (2025) Fftnet: fusing frequency and temporal awareness in long-term time series forecasting. Electronics 14(7):1303","journal-title":"Electronics"},{"issue":"2","key":"815_CR17","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/bdcc9020035","volume":"9","author":"K Yemets","year":"2025","unstructured":"Yemets K, Izonin I, Dronyuk I (2025) Enhancing the fft-lstm time-series forecasting model via a novel fft-based feature extraction\u2013extension scheme. Big Data Cognit Comput 9(2):35","journal-title":"Big Data And Cognit Comput"},{"key":"815_CR18","doi-asserted-by":"crossref","unstructured":"Zhao F, Lin W, Lin S, Zhong H, Li K (2024) Tfegru: time-frequency enhanced gated recurrent unit with attention for cloud workload prediction. In IEEE Transactions on Services Computing","DOI":"10.1109\/TSC.2024.3517324"},{"key":"815_CR19","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.comcom.2022.11.018","volume":"198","author":"J Dogani","year":"2023","unstructured":"Dogani J, Khunjush F, Seydali M (2023) Host load prediction in cloud computing with discrete wavelet transformation (dwt) and bidirectional gated recurrent unit (bigru) network. Comput Commun 198:157\u2013174","journal-title":"Comput Commun"},{"key":"815_CR20","doi-asserted-by":"crossref","unstructured":"Bi J, Ma H, Yuan H, Buyya R, Yang J, Zhang J, Zhou M (2024) Multivariate resource usage prediction with frequency-enhanced and attention-assisted transformer in cloud computing systems. In IEEE Internet of Things Journal","DOI":"10.1109\/JIOT.2024.3395610"},{"issue":"1","key":"815_CR21","doi-asserted-by":"publisher","first-page":"12557","DOI":"10.1038\/s41598-025-95529-2","volume":"15","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Zhou X, Zhang Y, Li S, Liu S (2025) Improving time series forecasting in frequency domain using a multi resolution dual branch mixer with noise insensitive arctanloss. Sci Rep 15(1):12557","journal-title":"Sci Rep"},{"key":"815_CR22","unstructured":"Yi K, Zhang Q, Fan W, Wang S, Wang P, He H, Lian D, An N, Cao L, Niu Z (2023) Frequency-domain mlps are more effective learners in time series forecasting. https:\/\/arxiv.org\/abs\/2311.06184"},{"issue":"1","key":"815_CR23","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/78.738253","volume":"47","author":"R Pintelon","year":"1999","unstructured":"Pintelon R, Schoukens J (1999) Time series analysis in the frequency domain. IEEE Trans on Signal Process 47(1):206\u2013210. https:\/\/doi.org\/10.1109\/78.738253","journal-title":"IEEE Trans On Signal Process"},{"key":"815_CR24","doi-asserted-by":"crossref","unstructured":"Reddy R (2024) Sustainable computing: a comprehensive review of energy-efficient algorithms and systems. TechRxiv Preprints","DOI":"10.36227\/techrxiv.172831425.52143965\/v1"},{"key":"815_CR25","doi-asserted-by":"publisher","first-page":"74011","DOI":"10.1109\/ACCESS.2024.3404222","volume":"12","author":"M Alhartomi","year":"2024","unstructured":"Alhartomi M, Salh A, Audah L, Alzahrani S, Alzahmi A (2024) Enhancing sustainable edge computing offloading via renewable prediction for energy harvesting. IEEE Access 12:74011\u201374023","journal-title":"IEEE Access"},{"key":"815_CR26","doi-asserted-by":"crossref","unstructured":"Clemm C, Stobbe L, Wimalawarne K, Druschke J (2024) Towards green ai: current status and future research. In 2024 Electronics Goes Green 2024+(EGG), IEEE, Berlin, Germany, 1\u201311","DOI":"10.23919\/EGG62010.2024.10631247"},{"key":"815_CR27","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Salt Lake City, UT, USA, 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"815_CR28","unstructured":"Nashold L, Krishnan R (2020) Using lstm and sarima models to forecast cluster cpu usage. arXiv preprint arXiv:2007.08092"},{"issue":"1","key":"815_CR29","first-page":"2782349","volume":"2019","author":"J Chen","year":"2019","unstructured":"Chen J, Wang Y (2019) A hybrid method for short-term host utilization prediction in cloud computing. J Electr And Comput Eng 2019(1):2782349","journal-title":"J Electr And Comput Eng"},{"issue":"1","key":"815_CR30","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s13677-023-00473-z","volume":"12","author":"AI Maiyza","year":"2023","unstructured":"Maiyza AI, Korany NO, Banawan K, Hassan HA, Sheta WM (2023) Vtgan: hybrid generative adversarial networks for cloud workload prediction. J Cloud Comput 12(1):97","journal-title":"J Cloud Comput"},{"key":"815_CR31","unstructured":"Liu Y, Zhang C, Song J, Chen S, Yin S, Wang Z, Zeng L, Cao Y, Jiao J (2025) Mofe-time: mixture of frequency domain experts for time-series forecasting models. arXiv preprint arXiv:2507.06502"},{"issue":"1","key":"815_CR32","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1109\/TSC.2024.3517324","volume":"18","author":"F Zhao","year":"2025","unstructured":"Zhao F, Lin W, Lin S, Zhong H, Li K (2025) Tfegru: time-frequency enhanced gated recurrent unit with attention for cloud workload prediction. IEEE Trans On Serv Comput 18(1):467\u2013478","journal-title":"IEEE Trans On Serv Comput"},{"issue":"1","key":"815_CR33","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Computation 3(1):79\u201387","journal-title":"Neural Computation"},{"key":"815_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30. Long Beach, California, USA, pp 5998\u20136008"},{"key":"815_CR35","unstructured":"Oppenheim AV, Schafer RW (2009) Discrete-time signal processing, 3rd edn. Prentice Hall, Upper Saddle River, NJ"},{"issue":"7","key":"815_CR36","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1002\/ese3.1450","volume":"11","author":"S Cen","year":"2023","unstructured":"Cen S, Kim DO, Lim CG (2023) A fused cnn-lstm model using fft with application to real-time power quality disturbances recognition. Energy Sciamp Eng 11(7):2267\u20132280","journal-title":"Energy Sciamp; Eng"},{"key":"815_CR37","doi-asserted-by":"crossref","unstructured":"Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? In Proceedings of the AAAI Conference on Artificial Intelligence, vol 37. pp 11121\u201311128","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"815_CR38","unstructured":"Wang S, Wu H, Shi X, Hu T, Luo H, Ma L, Zhang JY, Zhou J (2024) Timemixer: decomposable multiscale mixing for time series forecasting. arXiv preprint arXiv:2405.14616"},{"key":"815_CR39","doi-asserted-by":"crossref","unstructured":"Zhao F, Lin W, Lin S, Tang S, Li K (2025) Mscnet: multi-scale network with convolutions for long-term cloud workload prediction. In IEEE Transactions on Services Computing","DOI":"10.1109\/TSC.2025.3536313"},{"key":"815_CR40","unstructured":"Lin S, Lin W, Wu W, Chen H, Yang J (2024) Sparsetsf: modeling long-term time series forecasting with 1k parameters. arXiv preprint arXiv:2405.00946"},{"key":"815_CR41","doi-asserted-by":"crossref","unstructured":"Lin S, Lin W, Wu W, Wang S, Wang Y (2024) Petformer: long-term time series forecasting via placeholder-enhanced transformer. In IEEE Transactions on Emerging Topics in Computational Intelligence","DOI":"10.1109\/TETCI.2024.3502437"},{"issue":"2","key":"815_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1","journal-title":"Int J Data Min Knowl Manag Process"},{"key":"815_CR43","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-30599-8_20","volume-title":"Principles of performance and reliability modeling and evaluation: essays in honor of Kishor Trivedi on His 70th Birthday","author":"MC Calzarossa","year":"2016","unstructured":"Calzarossa MC, Della Vedova ML, Massari L, Petcu D, Tabash MI, Tessera D (2016) Workloads in the clouds. In: Principles of performance and reliability modeling and evaluation: essays in honor of Kishor Trivedi on His 70th Birthday. Springer, pp 525\u2013550"},{"issue":"4","key":"815_CR44","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0165-1684(90)90158-U","volume":"19","author":"P Duhamel","year":"1990","unstructured":"Duhamel P, Vetterli M (1990) Fast fourier transforms: a tutorial review and a state of the art. Signal Process 19(4):259\u2013299","journal-title":"Signal Process"},{"key":"815_CR45","volume-title":"The fourier transform and its application in machine learning.","author":"E Gomede","year":"2023","unstructured":"Gomede E (2023) The fourier transform and its application in machine learning. Medium, TDS Archive"},{"key":"815_CR46","unstructured":"Qiu S (2018) Global weighted average pooling bridges pixel-level localization and image-level classification. arXiv preprint arXiv:1809.08264"},{"issue":"10","key":"815_CR47","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2016) Lstm: a search space odyssey. IEEE Trans on Neural Networks And Learn Syst 28(10):2222\u20132232","journal-title":"IEEE Trans On Neural Networks And Learn Syst"},{"key":"815_CR48","unstructured":"(2018) Alibaba: Alibaba cluster Trace Data v2018. https:\/\/github.com\/alibaba\/ clusterdata\/tree\/master\/cluster-trace-v2018. Accessed: 2025-05-28"},{"key":"815_CR49","unstructured":"(2023) Entony: cloud computing performance metrics. Kaggle. Accessed:2025\u201305\u201328. https:\/\/www.kaggle.com\/dsv\/6165137"},{"issue":"1","key":"815_CR50","first-page":"51","volume":"445","author":"W McKinney","year":"2010","unstructured":"McKinney W et al. (2010) Data structures for statistical computing in python. scipy 445(1):51\u201356","journal-title":"scipy"},{"key":"815_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T (2009) The elements of statistical learning: data mining, inference, and prediction. Springer"},{"key":"815_CR52","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al. (2011) Scikit-learn: machine learning in python. The J Mach Learn Res 12:2825\u20132830","journal-title":"The J Mach Learn Res"},{"issue":"3","key":"815_CR53","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s12065-025-01053-7","volume":"18","author":"MA Jahin","year":"2025","unstructured":"Jahin MA, Shahriar A, Amin MA (2025) Mcdfn: supply chain demand forecasting via an explainable multi-channel data fusion network model. Evol Intel 18(3):66","journal-title":"Evol Intel"},{"issue":"7553","key":"815_CR54","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"8","key":"815_CR55","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735\u20131780","journal-title":"Neural Computation"},{"key":"815_CR56","doi-asserted-by":"crossref","unstructured":"Lea C, Flynn MD, Vidal R, Reiter A, Hager (2017) G.D. Temporal convolutional networks for action segmentation and detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 156\u2013165","DOI":"10.1109\/CVPR.2017.113"},{"key":"815_CR57","unstructured":"Yi K, Zhang Q, Fan W, Wang S, Wang P, He H, An N, Lian D, Cao L, Niu Z (2023) Frequency-domain mlps are more effective learners in time series forecasting. Adv Neural Inf Process Syst 36 76656\u201376679"},{"issue":"5","key":"815_CR58","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1145\/2890784","volume":"59","author":"B Burns","year":"2016","unstructured":"Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J (2016) Borg, omega, and kubernetes. Commun of The ACM 59(5):50\u201357. https:\/\/doi.org\/10.1145\/2890784","journal-title":"Commun Of The ACM"},{"key":"815_CR59","unstructured":"Volz J, Rabenstein B (2015) Prometheus: a next-generation monitoring system. In USENIX SREcon Europe 2015, Talk. https:\/\/www.usenix.org\/conference\/srecon15europe\/program\/presentation\/rabenstein"},{"key":"815_CR60","unstructured":"(2022). Dysnix: PredictKube: AI-based Kubernetes Autoscaler for cloud workloads. https:\/\/keda.sh\/blog\/2022-02-09-predictkube-scaler\/blog\/technical.case.study"},{"key":"815_CR61","doi-asserted-by":"publisher","first-page":"109768","DOI":"10.1109\/ACCESS.2022.3214985","volume":"10","author":"D-D Vu","year":"2022","unstructured":"Vu D-D, Tran M-N, Kim Y (2022) Predictive hybrid autoscaling for containerized applications. IEEE Access 10:109768\u2013109778","journal-title":"IEEE Access"},{"key":"815_CR62","doi-asserted-by":"crossref","unstructured":"Ju L, Singh P, Toor S (2021) Proactive autoscaling for edge computing systems with kubernetes. In Proceedings of the 14th IEEE\/ACM International Conference on Utility and Cloud Computing Companion, pp 1\u20138","DOI":"10.1145\/3492323.3495588"},{"key":"815_CR63","doi-asserted-by":"publisher","first-page":"1509165","DOI":"10.3389\/fcomp.2025.1509165","volume":"7","author":"PB Guruge","year":"2025","unstructured":"Guruge PB, Priyadarshana Y (2025) Time series forecasting-based kubernetes autoscaling using facebook prophet and long short-term memory. Front Comput Sci 7:1509165","journal-title":"Front Comput Sci"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-025-00815-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-025-00815-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-025-00815-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T10:59:52Z","timestamp":1766055592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13677-025-00815-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,11]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["815"],"URL":"https:\/\/doi.org\/10.1186\/s13677-025-00815-z","relation":{},"ISSN":["2192-113X"],"issn-type":[{"type":"electronic","value":"2192-113X"}],"subject":[],"published":{"date-parts":[[2025,12,11]]},"assertion":[{"value":"14 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Our source code is available at\n                      \n                      (accessed on November 2, 2025).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplementary information"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"75"}}