{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:36:02Z","timestamp":1786980962934,"version":"build-2736575974"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2015M3C4A7065646"],"award-info":[{"award-number":["2015M3C4A7065646"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2020R1H1A2011685"],"award-info":[{"award-number":["2020R1H1A2011685"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C1003379"],"award-info":[{"award-number":["2021R1A2C1003379"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10586-021-03299-z","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T05:23:44Z","timestamp":1622784224000},"page":"2577-2589","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Interference-aware execution framework with Co-scheML on GPU clusters"],"prefix":"10.1007","volume":"26","author":[{"given":"Sejin","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoonhee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"issue":"6","key":"3299_CR1","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1177\/1094342019846956","volume":"33","author":"G Aupy","year":"2019","unstructured":"Aupy, G., Benoit, A., Goglin, B., Pottier, L., Robert, Y.: Co-scheduling HPC workloads on cache-partitioned CMP platforms. Int. J. High Perform. Comput. Appl. 33(6), 1221\u20131239 (2019)","journal-title":"Int. J. High Perform. Comput. Appl."},{"key":"3299_CR2","doi-asserted-by":"crossref","unstructured":"Bao, Y., et al.: Deep learning-based job placement in distributed machine learning clusters. In: IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications (2019)","DOI":"10.1109\/INFOCOM.2019.8737460"},{"key":"3299_CR3","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Yang, S.R., et al.: A kubernetes-based monitoring platform for dynamic cloud resource provisioning. In: GLOBECOM 2017\u20142017 IEEE Global Communications Conference (2017)","DOI":"10.1109\/GLOCOM.2017.8254046"},{"key":"3299_CR4","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TPDS.2019.2931558","volume":"31","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Quan, W., et al.: Deep learning research and development platform: characterizing and scheduling with GOS guarantees on GPU clusters. IEEE Trans. Parallel Distrib. Syst. 31, 34\u201350 (2019)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"3299_CR5","doi-asserted-by":"crossref","unstructured":"Dauwe, D., Jonardi, E., Friese, R., Pasricha, S., Maciejewski, A.A., Bader, D.A., Siegel, H.J.: A methodology for co-location aware application performance modeling in multicore computing. In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, pp. 434\u2013443. IEEE (2015)","DOI":"10.1109\/IPDPSW.2015.38"},{"key":"3299_CR6","doi-asserted-by":"crossref","unstructured":"Diab, K.M., et\u00a0al.: Dynamic sharing of GPUS in cloud systems. In: IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum (2013)","DOI":"10.1109\/IPDPSW.2013.102"},{"key":"3299_CR7","unstructured":"DJINN: https:\/\/github.com\/LLNL\/DJINN"},{"key":"3299_CR8","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1007\/s10586-019-03037-6","volume":"23","author":"X Geng","year":"2020","unstructured":"Geng, X., Zhang, H., et al.: Interference-aware parallelization for deep learning workload in GPU cluster. Clust. Comput. 23, 2689\u20132702 (2020)","journal-title":"Clust. Comput."},{"key":"3299_CR9","doi-asserted-by":"crossref","unstructured":"Gu, J., et al.: Gaiagpu: Sharing GPUS in container clouds. In: IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom) (2018)","DOI":"10.1109\/BDCloud.2018.00077"},{"key":"3299_CR10","unstructured":"Gu, J., Chowdhury, M., et\u00a0al.: Tiresias: a GPU cluster manager for distributed deep learning. In: In16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19) (2019)"},{"issue":"12","key":"3299_CR11","doi-asserted-by":"publisher","first-page":"3472","DOI":"10.1109\/TPDS.2017.2717908","volume":"28","author":"C-H Hong","year":"2017","unstructured":"Hong, C.-H., et al.: FairGV: fair and fast GPU virtualization. IEEE Trans. Parallel Distrib. Syst. 28(12), 3472\u20133485 (2017)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"3299_CR12","unstructured":"InfuxDB: https:\/\/www.influxdata.com\/"},{"key":"3299_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Shen, X., Jie, C., Tripathi, R.: Analysis and approximation of optimal co-scheduling on chip multiprocessors. In: 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT), pp.\u00a0220\u2013229. IEEE (2008)","DOI":"10.1145\/1454115.1454146"},{"key":"3299_CR14","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, Y.: Co-scheml: interference-aware container co-scheduling scheme using machine learning application profiles for GPU clusters. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), pp.\u00a0104\u2013108. IEEE (2020)","DOI":"10.1109\/CLUSTER49012.2020.00020"},{"key":"3299_CR15","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, Y.: Toward interference-aware GPU container co-scheduling learning from application profiles. In: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). IEEE, pp.\u00a019\u201323 (2020)","DOI":"10.1109\/ACSOS-C51401.2020.00023"},{"key":"3299_CR16","unstructured":"Kubernetes: https:\/\/kubernetes.io\/docs\/tasks\/manage-gpus\/scheduling-gpus\/ (2020)"},{"key":"3299_CR17","unstructured":"LAMMPS-Molecular-Dynamics-Simulator: https:\/\/lammps.sandia.gov\/"},{"key":"3299_CR18","doi-asserted-by":"crossref","unstructured":"Liaw, R., Bhardwaj, R., et\u00a0al.: Hypersched: dynamic resource reallocation for model development on a deadline. In: Proceedings of the ACM Symposium on Cloud Computing (2019)","DOI":"10.1145\/3357223.3362719"},{"issue":"2","key":"3299_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2882783","volume":"34","author":"D Lo","year":"2016","unstructured":"Lo, D., Cheng, L., Govindaraju, R., Ranganathan, P., Kozyrakis, C.: Improving resource efficiency at scale with heracles. ACM Trans. Comput. Syst. (TOCS) 34(2), 1\u201333 (2016)","journal-title":"ACM Trans. Comput. Syst. (TOCS)"},{"key":"3299_CR20","doi-asserted-by":"crossref","unstructured":"Muralidhara, S.P., Subramanian, L., Mutlu, O., Kandemir, M., Moscibroda, T.: Reducing memory interference in multicore systems via application-aware memory channel partitioning. In: 2011 44th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO), pp.\u00a0374\u2013385. IEEE (2011)","DOI":"10.1145\/2155620.2155664"},{"key":"3299_CR21","unstructured":"NVIDIA-GPU-Container(NGC): https:\/\/ngc.nvidia.com\/"},{"key":"3299_CR22","unstructured":"NVIDIA-Multi-Process-Service: https:\/\/docs.nvidia.com\/deploy\/pdf\/CUDA-Multi-Process-Service-Overview.pdf (2019)"},{"key":"3299_CR23","unstructured":"NVIDIA-VGPU: https:\/\/docs.nvidia.com\/grid\/latest\/grid-vgpu-user-guide\/index.html"},{"key":"3299_CR24","unstructured":"Openstack: https:\/\/specs.openstack.org\/openstack\/nova-specs\/specs\/queens\/implemented\/add-support-for-vgpu.html"},{"key":"3299_CR25","doi-asserted-by":"crossref","unstructured":"Peng, Y., Bao, Y., et al.: Optimus: an efficient dynamic resource scheduler for deep learning clusters. In: Proceedings of the Thirteenth EuroSys Conference (2018)","DOI":"10.1145\/3190508.3190517"},{"key":"3299_CR26","unstructured":"QMCPACK: https:\/\/qmcpack.org\/"},{"key":"3299_CR27","doi-asserted-by":"crossref","unstructured":"Song, S., et\u00a0al.: Gaia scheduler: A kubernetes-based scheduler framework. In: IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom) (2018)","DOI":"10.1109\/BDCloud.2018.00048"},{"key":"3299_CR28","unstructured":"Tensorflow-CNN-benchmarks: https:\/\/github.com\/tensorflow\/benchmarks\/tree\/master\/scripts\/tf_c- nn_benchmarks"},{"key":"3299_CR29","doi-asserted-by":"crossref","unstructured":"Thinakaran, P., Gunasekaran, J.R., et\u00a0al.: Kube-knots: resource harvesting through dynamic container orchestration in gpu-based datacenters. In: 2019 IEEE International Conference on Cluster Computing (CLUSTER) (2019)","DOI":"10.1109\/CLUSTER.2019.8891040"},{"key":"3299_CR30","doi-asserted-by":"crossref","unstructured":"Ukidave, Y., et\u00a0al.: Mystic: predictive scheduling for gpu based cloud servers using machine learning. In: 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2016)","DOI":"10.1109\/IPDPS.2016.73"},{"key":"3299_CR31","doi-asserted-by":"crossref","unstructured":"Wen, Y., O\u2019Boyle, M.F., Fensch, C.: MaxPair: enhance OpenCL concurrent kernel execution by weighted maximum matching. In: Proceedings of the 11th Workshop on General Purpose GPUs (2018)","DOI":"10.1145\/3180270.3180272"},{"key":"3299_CR32","unstructured":"Xiao, W., et\u00a0al.: Gandiva: Introspective cluster scheduling for deep learning. In: 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) (2018)"},{"key":"3299_CR33","unstructured":"Xu, X., et\u00a0al.: Characterization and prediction of performance interference on mediated passthrough GPUs for interference-aware scheduler. In: 11th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 19) (2019)"},{"key":"3299_CR34","unstructured":"YARN: https:\/\/hadoop.apache.org\/docs\/r3.1.0\/hadoop-yarn\/hadoop-yarn-site\/UsingGpus.html (2018)"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03299-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-021-03299-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-021-03299-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T16:22:08Z","timestamp":1693066928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-021-03299-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["3299"],"URL":"https:\/\/doi.org\/10.1007\/s10586-021-03299-z","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,18]]},"assertion":[{"value":"4 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}