{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:50:13Z","timestamp":1769194213709,"version":"3.49.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031226977","type":"print"},{"value":"9783031226984","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-22698-4_3","type":"book-chapter","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T09:03:40Z","timestamp":1673427820000},"page":"47-67","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Toward Building a\u00a0Digital Twin of\u00a0Job Scheduling and\u00a0Power Management on\u00a0an\u00a0HPC System"],"prefix":"10.1007","author":[{"given":"Tatsuyoshi","family":"Ohmura","sequence":"first","affiliation":[]},{"given":"Yoichi","family":"Shimomura","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8966-867X","authenticated-orcid":false,"given":"Ryusuke","family":"Egawa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2858-3140","authenticated-orcid":false,"given":"Hiroyuki","family":"Takizawa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"17557","DOI":"10.1109\/ACCESS.2021.3053162","volume":"9","author":"M Agung","year":"2021","unstructured":"Agung, M., Watanabe, Y., Weber, H., Egawa, R., Takizawa, H.: Preemptive parallel job scheduling for heterogeneous systems supporting urgent computing. IEEE Access 9, 17557\u201317571 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3053162","journal-title":"IEEE Access"},{"key":"3_CR2","unstructured":"Baheri, B., Guan, Q.: Mars: Multi-scalable actor-critic reinforcement learning scheduler. ArXiv abs\/ arXiv: 2005.01584 (2020)"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Chahal, D., Mathew, B., Nambiar, M.: Simulation based job scheduling optimization for batch workloads. In: Proceedings of the 2019 ACM\/SPEC International Conference on Performance Engineering, ICPE 201, pp. 313\u2013320. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3297663.3310312","DOI":"10.1145\/3297663.3310312"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Cunha, R.L.F., Chaimowicz, L.: Towards a common environment for learning scheduling algorithms. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 1\u20138 (2020)","DOI":"10.1109\/MASCOTS50786.2020.9285940"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-319-61756-5_10","volume-title":"Job Scheduling Strategies for Parallel Processing","author":"PF Dutot","year":"2017","unstructured":"Dutot, P.F., Mercier, M., Poquet, M., Richard, O.: Batsim: A realistic language-independent resources and jobs management systems simulator. In: Desai, N., Cirne, W. (eds.) Job Scheduling Strategies for Parallel Processing, pp. 178\u2013197. Springer International Publishing, Cham (2017)"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Egawa, R., et al.: Exploiting the potentials of the second generation sx-aurora tsubasa. In: 2020 IEEE\/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)m pp. 39\u201349 (2020). https:\/\/doi.org\/10.1109\/PMBS51919.2020.00010","DOI":"10.1109\/PMBS51919.2020.00010"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lan, Z., Childers, T., Rich, P., Allcock, W., Papka, M.E.: Deep reinforcement agent for scheduling in hpc (2021)","DOI":"10.1109\/IPDPS49936.2021.00090"},{"key":"3_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/3-540-45706-2_4","volume-title":"Euro-Par 2002 Parallel Processing","author":"DG Feitelson","year":"2002","unstructured":"Feitelson, D.G.: The forgotten factor: facts on performance evaluation and its dependence on workloads. In: Monien, B., Feldmann, R. (eds.) Euro-Par 2002. LNCS, vol. 2400, pp. 49\u201360. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45706-2_4"},{"issue":"10","key":"3_CR9","doi-asserted-by":"publisher","first-page":"2304","DOI":"10.1109\/TPDS.2018.2820699","volume":"29","author":"E Gaussier","year":"2018","unstructured":"Gaussier, E., Lelong, J., Reis, V., Trystram, D.: Online tuning of easy-backfilling using queue reordering policies. IEEE Trans. Parallel Distrib. Syst. 29(10), 2304\u20132316 (2018). https:\/\/doi.org\/10.1109\/TPDS.2018.2820699","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"3_CR10","unstructured":"IBM Spectrum LSF Suites: https:\/\/www.ibm.com\/products\/hpc-workload-management"},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Klus\u00e0\u010dek, D., Rudov\u00e0, H.: Alea 2 - job scheduling simulator. SIMUTools 2010\u20133rd International ICST Conference on Simulation Tools and Techniques, p. 61 (01 2010). https:\/\/doi.org\/10.4108\/ICST.SIMUTOOLS2010.8722","DOI":"10.4108\/ICST.SIMUTOOLS2010.8722"},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Komatsu, K., et al.: Performance evaluation of a vector supercomputer sx-aurora tsubasa. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 685\u2013696 (2018). https:\/\/doi.org\/10.1109\/SC.2018.00057","DOI":"10.1109\/SC.2018.00057"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Kondameedi, V., Vadhiyar, S.S.: Adaptive hybrid queue configuration for supercomputer systems. In: 2017 17th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 90\u201399 (2017)","DOI":"10.1109\/CCGRID.2017.80"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Krishnamurthy, D., Alemzadeh, M., Moussavi, M.: Towards automated hpc scheduler configuration tuning. In: Concurrency and Computation: Practice and Experience 23 (2011)","DOI":"10.1002\/cpe.1730"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Maiterth, M., et al.: Energy and power aware job scheduling and resource management: Global survey - initial analysis. In: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 685\u2013693 (2018). https:\/\/doi.org\/10.1109\/IPDPSW.2018.00111","DOI":"10.1109\/IPDPSW.2018.00111"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Mao, H., Alizadeh, M., Menache, I., Kandula, S.: Resource management with deep reinforcement learning. In: Proceedings of the 15th ACM Workshop on Hot Topics in Networks, HotNets 2016, pp. 50\u201356. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/3005745.3005750","DOI":"10.1145\/3005745.3005750"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Mao, H., Schwarzkopf, M., Venkatakrishnan, S.B., Meng, Z., Alizadeh, M.: Learning scheduling algorithms for data processing clusters. In: Proceedings of the ACM Special Interest Group on Data Communication, SIGCOMM 2019, pp. 270\u2013288. Association for Computing Machinery (2019). https:\/\/doi.org\/10.1145\/3341302.3342080,https:\/\/doi.org\/10.1145\/3341302.3342080","DOI":"10.1145\/3341302.3342080, 10.1145\/3341302.3342080"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/71.932708","volume":"12","author":"A Mu\u2019alem","year":"2001","unstructured":"Mu\u2019alem, A., Feitelson, D.: Utilization, predictability, workloads, and user runtime estimates in scheduling the ibm sp2 with backfilling. IEEE Trans. Paral. Distrib. Syst. 12, 529\u2013543 (2001). https:\/\/doi.org\/10.1109\/71.932708","journal-title":"IEEE Trans. Paral. Distrib. Syst."},{"key":"3_CR19","unstructured":"NEC Network Queuing System V (NQSV) User\u2019s Guide [Introduction]. https:\/\/www.hpc.nec\/documents\/nqsv\/pdfs\/g2ad01e-NQSVUG-Introduction.pdf"},{"key":"3_CR20","unstructured":"NEC SX-Aurora TSUBASA. https:\/\/www.nec.com\/en\/global\/solutions\/hpc\/sx"},{"key":"3_CR21","unstructured":"OpenPBS. https:\/\/www.openpbs.org\/"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Peng, Y., Bao, Y., Chen, Y., wu, C., Meng, C., Lin, W.: Dl2: A deep learning-driven scheduler for deep learning clusters. IEEE Trans. Paral. Distri. Syst. 1\u20131 (2021). https:\/\/doi.org\/10.1109\/TPDS.2021.3052895","DOI":"10.1109\/TPDS.2021.3052895"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Powers, S.: A study of the impact of scheduling parameters in heterogeneous computing environments. In: Proceedings - Winter Simulation Conference, vol. 2015, pp. 933\u2013942, Jan 2015. https:\/\/doi.org\/10.1109\/WSC.2014.7019953","DOI":"10.1109\/WSC.2014.7019953"},{"key":"3_CR24","unstructured":"Ryu, B., An, A., Rashidi, Z., Liu, J., Hu, Y.: Towards topology aware pre-emptive job scheduling with deep reinforcement learning. In: Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering, CASCON 2020, pp. 83\u201392. IBM Corp., USA (2020)"},{"key":"3_CR25","doi-asserted-by":"publisher","unstructured":"Simakov, N., et al.: A slurm simulator: implementation and parametric analysis, pp. 197\u2013217. In: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, Jan 2018. https:\/\/doi.org\/10.1007\/978-3-319-72971-8_10","DOI":"10.1007\/978-3-319-72971-8_10"},{"key":"3_CR26","unstructured":"SLURM. https:\/\/www.schedmd.com\/"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1002\/cpe.1307","volume":"20","author":"A Sulistio","year":"2008","unstructured":"Sulistio, A., Cibej, U., Venugopal, S., Robic, B., Buyya, R.: A toolkit for modelling and simulating data grids: An extension to gridsim. Concur. Comput. Pract. Exp. 20, 1591\u20131609 (2008). https:\/\/doi.org\/10.1002\/cpe.1307","journal-title":"Concur. Comput. Pract. Exp."},{"key":"3_CR28","unstructured":"SX-Aurora TSUBASA Architecture Guide Revision 1.1. https:\/\/www.hpc.nec\/documents\/guide\/pdfs\/Aurora_ISA_guide.pdf"},{"key":"3_CR29","unstructured":"Top500. https:\/\/www.top500.org\/"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, D., Dai, D., He, Y., Bao, F.S., Xie, B.: Rlscheduler: An automated hpc batch job scheduler using reinforcement learning. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020. IEEE Press (2020)","DOI":"10.1109\/SC41405.2020.00035"}],"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\/978-3-031-22698-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T23:57:01Z","timestamp":1728691021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22698-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031226977","9783031226984"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22698-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 January 2023","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsspp2022","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"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1 Keynote paper","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}