{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:59:06Z","timestamp":1767322746443,"version":"3.48.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032105066","type":"print"},{"value":"9783032105073","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-10507-3_2","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:54:21Z","timestamp":1767322461000},"page":"21-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Job Grouping Based Intelligent Resource Prediction Framework"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3731-8425","authenticated-orcid":false,"given":"Beste","family":"Oztop","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9282-8977","authenticated-orcid":false,"given":"Benjamin","family":"Schwaller","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3950-1626","authenticated-orcid":false,"given":"Vitus J.","family":"Leung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8605-5795","authenticated-orcid":false,"given":"Jim","family":"Brandt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1704-3838","authenticated-orcid":false,"given":"Brian","family":"Kulis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5038-2682","authenticated-orcid":false,"given":"Manuel","family":"Egele","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-088X","authenticated-orcid":false,"given":"Ayse K.","family":"Coskun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"2_CR1","unstructured":"Altair Grid Engine Homepage. https:\/\/altair.com\/grid-engine"},{"key":"2_CR2","unstructured":"Altair PBS Professional Homepage. https:\/\/altair.com\/pbs-professional"},{"key":"2_CR3","unstructured":"National Renewable Energy Laboratory (NREL): HPC User Facility. https:\/\/www.nrel.gov\/computational-science\/hpc-user-facility.html"},{"key":"2_CR4","unstructured":"Slurm Workload Manager Documentation. https:\/\/slurm.schedmd.com"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Antici, F., Bartolini, A., Domke, J., Kiziltan, Z., Yamamoto, K.: F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems (2024). https:\/\/doi.org\/10.5281\/zenodo.11467483","DOI":"10.5281\/zenodo.11467483"},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s41597-023-02174-3","volume":"10","author":"A Borghesi","year":"2023","unstructured":"Borghesi, A., et al.: M100 ExaData: a data collection campaign on the CINECA\u2019s Marconi100 Tier-0 supercomputer. Sci. Data 10(1), 288 (2023)","journal-title":"Sci. Data"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Borghesi, A., et al.: M100 dataset 5: from 22-01 to 22-02 (2023). https:\/\/doi.org\/10.5281\/zenodo.7589942","DOI":"10.5281\/zenodo.7589942"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785\u2013794. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Cui, H., Takahashi, K., Shimomura, Y., Takizawa, H.: Clustering based job runtime prediction for backfilling using classification. In: Workshop on Job Scheduling Strategies for Parallel Processing, pp. 40\u201359. Springer (2024)","DOI":"10.1007\/978-3-031-74430-3_3"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Dai, Y., et al.: Towards scalable resource management for supercomputers. In: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201315 (2022). https:\/\/doi.org\/10.1109\/SC41404.2022.00029","DOI":"10.1109\/SC41404.2022.00029"},{"issue":"10","key":"2_CR11","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1038\/s42254-024-00750-z","volume":"6","author":"J Dongarra","year":"2024","unstructured":"Dongarra, J., Keyes, D.: The co-evolution of computational physics and high-performance computing. Nat. Rev. Phys. 6(10), 621\u2013627 (2024)","journal-title":"Nat. Rev. Phys."},{"key":"2_CR12","unstructured":"Duplyakin, D., Menear, K.: NREL Eagle supercomputer jobs (2023). https:\/\/data.openei.org\/submissions\/5860"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"3","key":"2_CR14","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/TPDS.2020.3025914","volume":"32","author":"S Kardani-Moghaddam","year":"2021","unstructured":"Kardani-Moghaddam, S., Buyya, R., Ramamohanarao, K.: ADRL: a hybrid anomaly-aware deep reinforcement learning-based resource scaling in clouds. IEEE Trans. Parallel Distrib. Syst. 32(3), 514\u2013526 (2021). https:\/\/doi.org\/10.1109\/TPDS.2020.3025914","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Kurth, T., et al.: Exascale deep learning for climate analytics. In: SC 2018: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 649\u2013660 (2018). https:\/\/doi.org\/10.1109\/SC.2018.00054","DOI":"10.1109\/SC.2018.00054"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Lamar, K., Goponenko, A., Peterson, C., Allan, B.A., Brandt, J.M., Dechev, D.: Backfilling HPC jobs with a multimodal-aware predictor. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 618\u2013622 (2021). https:\/\/doi.org\/10.1109\/Cluster48925.2021.00093","DOI":"10.1109\/Cluster48925.2021.00093"},{"key":"2_CR17","unstructured":"Menear, K., Duplyakin, D.: Eagle Jobs (2024). https:\/\/github.com\/NREL\/eagle-jobs\/tree\/master"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Menear, K., Nag, A., Perr-Sauer, J., Lunacek, M., Potter, K., Duplyakin, D.: Mastering HPC runtime prediction: from observing patterns to a methodological approach. In: Practice and Experience in Advanced Research Computing 2023: Computing for the Common Good, PEARC 2023, pp. 75\u201385. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3569951.3593598","DOI":"10.1145\/3569951.3593598"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Newaz, M.N., Mollah, M.A.: Memory usage prediction of HPC workloads using feature engineering and machine learning. In: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, HPCAsia 2023, pp. 64\u201374. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3578178.3578241","DOI":"10.1145\/3578178.3578241"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Pal, A., Malakar, P.: An integrated job monitor, analyzer and predictor. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), pp. 609\u2013617 (2021). https:\/\/doi.org\/10.1109\/Cluster48925.2021.00091","DOI":"10.1109\/Cluster48925.2021.00091"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Patil, S., Devrani, R., J, B.: The potential of HPC in enhancing AI-driven comparative genomics studies. In: 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT), pp. 1053\u20131059 (2024). https:\/\/doi.org\/10.1109\/CSNT60213.2024.10545899","DOI":"10.1109\/CSNT60213.2024.10545899"},{"key":"2_CR22","unstructured":"Pedregosa, F., et\u00a0al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"2_CR23","unstructured":"Strohmaier, E., Dongarra, J., Simon, H., Meuer, M.: Top500 List (2024). https:\/\/www.top500.org"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Tanash, M., Yang, H., Andresen, D., Hsu, W.: Ensemble prediction of job resources to improve system performance for slurm-based HPC systems. In: Practice and Experience in Advanced Research Computing, pp.\u00a01\u20138 (2021). https:\/\/dl.acm.org\/doi\/10.1145\/3437359.3465574","DOI":"10.1145\/3437359.3465574"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Thonglek, K., Ichikawa, K., Takahashi, K., Iida, H., Nakasan, C.: Improving resource utilization in data centers using an LSTM-based prediction model. In: 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp.\u00a01\u20138 (2019). https:\/\/doi.org\/10.1109\/CLUSTER.2019.8891022","DOI":"10.1109\/CLUSTER.2019.8891022"},{"key":"2_CR26","unstructured":"Wilkes, J.: Yet more Google Compute Cluster Trace Data (2020). https:\/\/ai.googleblog.com\/2020\/04\/yet-more-google-compute-cluster-trace.html"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Hua, W., Zhou, Z., Suh, G.E., Delimitrou, C.: Sinan: ML-based and QoS-aware resource management for cloud microservices. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2021, pp. 167\u2013181. Association for Computing Machinery, New York (2021). https:\/\/doi.org\/10.1145\/3445814.3446693","DOI":"10.1145\/3445814.3446693"}],"container-title":["Lecture Notes in Computer Science","Job Scheduling Strategies for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10507-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:54:23Z","timestamp":1767322463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10507-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032105066","9783032105073"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10507-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Job Scheduling Strategies for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsspp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/jsspp.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}