{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:53:27Z","timestamp":1775634807141,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819584017","type":"print"},{"value":"9789819584024","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\/978-981-95-8402-4_10","type":"book-chapter","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:16:21Z","timestamp":1775632581000},"page":"185-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Framework for\u00a0Energy-Aware Prediction and\u00a0Scheduling in\u00a0Heterogeneous HPC Clusters"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1823-2509","authenticated-orcid":false,"given":"Hailong","family":"Shan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7798-7438","authenticated-orcid":false,"given":"Xiangyu","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9540-4570","authenticated-orcid":false,"given":"Haoran","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2023.104797","volume":"185","author":"M Mor\u00e1n","year":"2024","unstructured":"Mor\u00e1n, M., Balladini, J., Rexachs, D., Rucci, E.: Exploring energy saving opportunities in fault tolerant HPC systems. J. Parallel Distrib. Comput. 185, 104797 (2024)","journal-title":"J. Parallel Distrib. Comput."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Acun, F., Zhao, Z., Austin, B., Coskun, A.K., Wright, N.J.: Analysis of power consumption and GPU power capping for MILC. In: SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1856\u20131861. IEEE (2024)","DOI":"10.1109\/SCW63240.2024.00232"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Antici, F., Borghesi, A., Domke, J., Kiziltan, Z.: UoPC: a user-based online framework to predict job power consumption in HPC systems. In: ISC High Performance 2025 Research Paper Proceedings (40th International Conference), pp. 1\u201312 (2025)","DOI":"10.23919\/ISC.2025.11017729"},{"key":"10_CR4","unstructured":"Nana, R., Tadonki, C., Dokl\u00e1dal, P., Mesri, Y.: Energy concerns with HPC systems and applications. arXiv preprint arXiv:2309.08615 (2023)"},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2025.107760","volume":"167","author":"R Chen","year":"2025","unstructured":"Chen, R., Lin, W., Huang, H., Ye, X., Peng, Z.: GAS-MARL: green-aware job scheduling algorithm for HPC clusters based on multi-action deep reinforcement learning. Future Gener. Comput. Syst. 167, 107760 (2025)","journal-title":"Future Gener. Comput. Syst."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Beena, B.M., Prashanth, C.S.R., Manideep, T.S.S., Saragadam, S., Karthik, G.: A green cloud-based framework for energy-efficient task scheduling using carbon intensity data for heterogeneous cloud servers. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3562882"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Carastan-Santos, D., Da Costa, G., Poquet, M., Stolf, P., Trystram, D.: Light-weight prediction for improving energy consumption in HPC platforms. In: Euro-Par 2024, pp. 152\u2013165. Springer (2024)","DOI":"10.1007\/978-3-031-69577-3_11"},{"issue":"1","key":"10_CR8","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TSC.2017.2648791","volume":"13","author":"NT Hieu","year":"2017","unstructured":"Hieu, N.T., Di Francesco, M., Yl\u00e4-J\u00e4\u00e4ski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. 13(1), 186\u2013199 (2017)","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"7","key":"10_CR9","doi-asserted-by":"publisher","first-page":"9554","DOI":"10.1007\/s11227-023-05807-x","volume":"80","author":"S Wang","year":"2024","unstructured":"Wang, S., Chen, S., Shi, Y.: Utilization-prediction-aware energy optimization approach for heterogeneous GPU clusters. J. Supercomput. 80(7), 9554\u20139578 (2024)","journal-title":"J. Supercomput."},{"issue":"9","key":"10_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4410","volume":"30","author":"A S\u00eerbu","year":"2018","unstructured":"S\u00eerbu, A., Babaoglu, O.: A data-driven approach to modeling power consumption for a hybrid supercomputer. Concurr. Comput. Pract. Exp. 30(9), e4410 (2018)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"5","key":"10_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.3390\/fi17050203","volume":"17","author":"M Artioli","year":"2025","unstructured":"Artioli, M., et al.: C6EnPLS: a high-performance computing job dataset for the analysis of linear solvers\u2019 power consumption. Future Internet 17(5), 203 (2025)","journal-title":"Future Internet"},{"key":"10_CR12","unstructured":"Gu, J.: Characterization and Modelling of Resource Usage and Energy Consumption in HPC Datacenters by Machine Learning (2023)"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Goponenko, A.V., Lamar, K., Allan, B.A., Brandt, J.M., Dechev, D.: Job scheduling for HPC clusters: constraint programming vs. backfilling approaches. In: Proceedings of 18th ACM International Conference on Distributed and Event-Based Systems (DEBS), pp. 135\u2013146 (2024)","DOI":"10.1145\/3629104.3666038"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Abraham, S., Paul, A.K., Khan, R.I.S., Butt, A.R.: On the use of containers in high performance computing environments. In: Proceedings of 13th IEEE International Conference on Cloud Computing (CLOUD), pp. 284\u2013293 (2020)","DOI":"10.1109\/CLOUD49709.2020.00048"},{"issue":"2","key":"10_CR15","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/TSUSC.2022.3217014","volume":"8","author":"K Li","year":"2022","unstructured":"Li, K.: Design and analysis of heuristic algorithms for energy-constrained task scheduling with device-edge-cloud fusion. IEEE Trans. Sustain. Comput. 8(2), 208\u2013221 (2022)","journal-title":"IEEE Trans. Sustain. Comput."},{"issue":"6","key":"10_CR16","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TC.2011.68","volume":"60","author":"X Zhu","year":"2011","unstructured":"Zhu, X., Qin, X., Qiu, M.: QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters. IEEE Trans. Comput. 60(6), 800\u2013812 (2011)","journal-title":"IEEE Trans. Comput."},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1007\/s11227-018-2368-8","volume":"74","author":"L Yu","year":"2018","unstructured":"Yu, L., Zhou, Z., Fan, Y., Papka, M.E., Lan, Z.: System-wide trade-off modeling of performance, power, and resilience on petascale systems. J. Supercomput. 74, 3168\u20133192 (2018)","journal-title":"J. Supercomput."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Patrou, M., et al.: Power-Capping Metric Evaluation for Improving Energy Efficiency. arXiv preprint arXiv:2505.21758 (2025)","DOI":"10.1007\/978-3-032-07612-0_18"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Hossain, A., Abdurahman, A., Islam, M.A., Ahmed, K.: Power-Aware Scheduling for Multi-Center HPC Electricity Cost Optimization. arXiv preprint arXiv:2503.11011 (2025)","DOI":"10.1007\/978-3-032-10507-3_1"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jpdc.2019.07.007","volume":"134","author":"E Garc\u00eda-Mart\u00edn","year":"2019","unstructured":"Garc\u00eda-Mart\u00edn, E., Rodrigues, C.F., Riley, G., Grahn, H.: Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75\u201388 (2019)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"4","key":"10_CR21","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TPDS.2019.2953745","volume":"31","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Hu, J., Min, G., Zomaya, A.Y., El-Ghazawi, T.: Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans. Parallel Distrib. Syst. 31(4), 923\u2013934 (2019)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"2","key":"10_CR22","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s11269-024-03972-z","volume":"39","author":"X Pan","year":"2025","unstructured":"Pan, X., et al.: LSTM model-based rapid prediction method of urban inundation with rainfall time series. Water Resour. Manag. 39(2), 661\u2013688 (2025)","journal-title":"Water Resour. Manag."},{"key":"10_CR23","unstructured":"Wen, Q., et al.: Transformers in time series: a survey. arXiv preprint arXiv:2202.07125 (2022)"},{"key":"10_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103436","volume":"157","author":"ZH Meybodi","year":"2024","unstructured":"Meybodi, Z.H., et al.: Multi-content time-series popularity prediction with multiple-model transformers in MEC networks. Ad Hoc Netw. 157, 103436 (2024)","journal-title":"Ad Hoc Netw."},{"key":"10_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2025.103283","volume":"65","author":"S Ma","year":"2025","unstructured":"Ma, S., Liu, Y., Liu, Y., Wang, J., Fang, Q., Huang, Y.: Artificial intelligence-enabled predictive energy saving planning of liquid cooling system for data centers. Adv. Eng. Inform. 65, 103283 (2025)","journal-title":"Adv. Eng. Inform."},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, H., Wang, J., Qian, Y., Li, Q.: Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM. Energy 294, 130875 (2024). https:\/\/doi.org\/10.1016\/j.energy.2024.130875","DOI":"10.1016\/j.energy.2024.130875"},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s00607-024-01366-y","volume":"107","author":"Z Zuo","year":"2025","unstructured":"Zuo, Z., Huang, Y., Li, Z., Jiang, Y., Liu, C.: Mixed contrastive transfer learning for few-shot workload prediction in the cloud. Computing 107(1), 5 (2025)","journal-title":"Computing"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Stojkovic, J., Zhang, C., Goiri, \u00cd., Torrellas, J., Choukse, E.: Dynamollm: designing LLM inference clusters for performance and energy efficiency. In: Proceedings of 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 1348\u20131362 (2025)","DOI":"10.1109\/HPCA61900.2025.00102"},{"issue":"10","key":"10_CR29","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.1016\/j.jpdc.2014.06.008","volume":"74","author":"H Casanova","year":"2014","unstructured":"Casanova, H., Giersch, A., Legrand, A., Quinson, M., Suter, F.: Versatile, scalable, and accurate simulation of distributed applications and platforms. J. Parallel Distrib. Comput. 74(10), 2899\u20132917 (2014)","journal-title":"J. Parallel Distrib. Comput."},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Dutot, P.-F., Mercier, M., Poquet, M., Richard, O.: Batsim: a realistic language-independent resources and jobs management systems simulator. In: Workshop on Job Scheduling Strategies for Parallel Processing, pp. 178\u2013197. Springer (2015)","DOI":"10.1007\/978-3-319-61756-5_10"},{"issue":"1","key":"10_CR31","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"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-8402-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:16:29Z","timestamp":1775632589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-8402-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819584017","9789819584024"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-8402-4_10","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":"9 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"30 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","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":"ica3pp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-cybermatics.org\/2025\/ica3pp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}