{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:46:56Z","timestamp":1743040016957,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031736162"},{"type":"electronic","value":"9783031736179"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73617-9_22","type":"book-chapter","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T09:20:23Z","timestamp":1734600023000},"page":"276-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning-Based Algorithm for Predicting the Turning Point of Cloud Workload"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7741-0786","authenticated-orcid":false,"given":"Anmol","family":"Jain","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3242-525X","authenticated-orcid":false,"given":"Sanjaya Kumar","family":"Panda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1109\/TCC.2022.3160228","volume":"11","author":"L Ruan","year":"2022","unstructured":"Ruan, L., et al.: Cloud workload turning points prediction via cloud feature-enhanced deep learning. IEEE Trans. Cloud Comput. 11, 1719 (2022)","journal-title":"IEEE Trans. Cloud Comput."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10796-017-9742-6","volume":"21","author":"SK Panda","year":"2019","unstructured":"Panda, S.K., Gupta, I., Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front 21, 241\u2013259 (2019)","journal-title":"Inf. Syst. Front"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Panda, S.K., Jana, P.K.: An efficient resource allocation algorithm for IAAS cloud. In: Distributed Computing and Internet Technology: 11th International Conference, ICDCIT 2015, Bhubaneswar, India, February 5\u20138, 2015. Proceedings 11, pp. 351\u2013355. Springer (2015)","DOI":"10.1007\/978-3-319-14977-6_37"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Panda, S.K., Jana, P.K.: An efficient request-based virtual machine placement algorithm for cloud computing. In: Distributed Computing and Internet Technology: 13th International Conference, ICDCIT 2017, Bhubaneswar, India, January 13\u201316, 2017, Proceedings 13, pp. 129\u2013143. Springer (2017)","DOI":"10.1007\/978-3-319-50472-8_11"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Panda, S.K., Jana, P.K.: An efficient energy saving task consolidation algorithm for cloud computing systems. In: 2014 International Conference on Parallel, Distributed and Grid Computing, pp. 262\u2013267. IEEE (2014)","DOI":"10.1109\/PDGC.2014.7030753"},{"issue":"8","key":"22_CR6","doi-asserted-by":"publisher","first-page":"5329","DOI":"10.1109\/TITS.2020.3021075","volume":"22","author":"SK Pande","year":"2020","unstructured":"Pande, S.K., et al.: A smart cloud service management algorithm for vehicular clouds. IEEE Trans. Intell. Transp. Syst. 22(8), 5329\u20135340 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.1007\/s13369-016-2069-7","volume":"41","author":"SK Panda","year":"2016","unstructured":"Panda, S.K., Jana, P.K.: Uncertainty-based QOS min-min algorithm for heterogeneous multi-cloud environment. Arab. J. Sci. Eng. 41, 3003\u20133025 (2016)","journal-title":"Arab. J. Sci. Eng."},{"issue":"4","key":"22_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/data3040038","volume":"3","author":"A Hussain","year":"2018","unstructured":"Hussain, A., Aleem, M.: GoCJ: Google cloud jobs dataset for distributed and cloud computing infrastructures. Data 3(4), 38 (2018)","journal-title":"Data"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.future.2017.10.047","volume":"81","author":"Jitendra Kumar and Ashutosh Kumar Singh","year":"2018","unstructured":"Jitendra Kumar and Ashutosh Kumar Singh: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener. Comput. Syst. 81, 41\u201352 (2018)","journal-title":"Future Gener. Comput. Syst."},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Hsu, Y.-F., Matsuda, K., Matsuoka, M.: Self-aware workload forecasting in data center power prediction. In: 2018 18th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 321\u2013330. IEEE (2018)","DOI":"10.1109\/CCGRID.2018.00047"},{"issue":"3","key":"22_CR11","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1109\/TCC.2020.2998017","volume":"10","author":"IK Kim","year":"2020","unstructured":"Kim, I.K., Wang, W., Qi, Y., Humphrey, M.: Forecasting cloud application workloads with cloudinsight for predictive resource management. IEEE Trans. Cloud Comput. 10(3), 1848\u20131863 (2020)","journal-title":"IEEE Trans. Cloud Comput."},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neucom.2020.11.011","volume":"424","author":"J Bi","year":"2021","unstructured":"Bi, J., Li, S., Yuan, H., Zhou, M.C.: Integrated deep learning method for workload and resource prediction in cloud systems. Neurocomputing 424, 35\u201348 (2021)","journal-title":"Neurocomputing"},{"issue":"4","key":"22_CR13","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1109\/TNSM.2020.3013922","volume":"17","author":"S Gupta","year":"2020","unstructured":"Gupta, S., Dileep, A.D., Gonsalves, T.A.: Online sparse BLSTM models for resource usage prediction in cloud datacentres. IEEE Trans. Netw. Serv. Manag. 17(4), 2335\u20132349 (2020)","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"issue":"4","key":"22_CR14","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1002\/spe.2635","volume":"49","author":"M Duggan","year":"2019","unstructured":"Duggan, M., Shaw, R., Duggan, J., Howley, E., Barrett, E.: A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers. Softw. Pract. Exp. 49(4), 617\u2013639 (2019)","journal-title":"Softw. Pract. Exp."},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Di, S., Kondo, D., Cirne, W.: Host load prediction in a google compute cloud with a Bayesian model. In: SC\u201912: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp. 1\u201311. IEEE (2012)","DOI":"10.1109\/SC.2012.68"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Gao, M., Li, Y., Yu, J.: Workload prediction of cloud workflow based on graph neural network. In: Web Information Systems and Applications: 18th International Conference, WISA 2021, Kaifeng, China, September 24\u201326, 2021, Proceedings 18, pp. 169\u2013189. Springer (2021)","DOI":"10.1007\/978-3-030-87571-8_15"},{"issue":"4","key":"22_CR17","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TSC.2018.2804916","volume":"14","author":"B Xia","year":"2018","unstructured":"Xia, B., Li, T., Zhou, Q., Li, Q., Zhang, H.: An effective classification-based framework for predicting cloud capacity demand in cloud services. IEEE Trans. Serv. Comput. 14(4), 944\u2013956 (2018)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"7","key":"22_CR18","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Yu Yong","year":"2019","unstructured":"Yong, Yu., Si, X., Changhua, H., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"issue":"10","key":"22_CR19","doi-asserted-by":"publisher","first-page":"7585","DOI":"10.1016\/j.aej.2022.01.011","volume":"61","author":"KE ArunKumar","year":"2022","unstructured":"ArunKumar, K.E., Kalaga, D.V., Kumar, C.M.S., Kawaji, M., Brenza, T.M.: Comparative analysis of gated recurrent units (GRU), long short-term memory (LSTM) cells, autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA) for forecasting covid-19 trends. Alex. Eng. J. 61(10), 7585\u20137603 (2022)","journal-title":"Alex. Eng. J."},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"100681","DOI":"10.1016\/j.iot.2023.100681","volume":"21","author":"P Kumar","year":"2023","unstructured":"Kumar, P., Suresh, S.: Deeptranshar: a novel clustering-based transfer learning approach for recognizing the cross-domain human activities using GRUS (gated recurrent units) networks. Internet Things 21, 100681 (2023)","journal-title":"Internet Things"},{"key":"22_CR21","unstructured":"Datasets. https:\/\/cloud.google.com\/datasets. Accessed on 30 Jan 2024"},{"key":"22_CR22","unstructured":"Understanding Cross-Entropy Loss and Its Role in Classification Problems. https:\/\/medium.com\/@l228104\/understanding-cross-entropy-loss-and-its-role-in-classification-problems-d2550f2caad5. Accessed on 30 Jan 2024"}],"container-title":["IFIP Advances in Information and Communication Technology","Computer, Communication, and Signal Processing. Smart Solutions Towards SDG"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73617-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:06:00Z","timestamp":1734602760000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73617-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9783031736162","9783031736179"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73617-9_22","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2024,12,20]]},"assertion":[{"value":"20 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer, Communication, and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 March 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 March 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icccsp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icccsp.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}