{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:19:33Z","timestamp":1740122373091,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["R0190-16-2012"],"award-info":[{"award-number":["R0190-16-2012"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1A2B4004513","NRF-2016M3C4A7952587"],"award-info":[{"award-number":["NRF-2017R1A2B4004513","NRF-2016M3C4A7952587"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["21A20151113068"],"award-info":[{"award-number":["21A20151113068"]}],"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":[[2021,3]]},"DOI":"10.1007\/s10586-020-03191-2","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T10:03:13Z","timestamp":1612173793000},"page":"181-193","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["OMBM-ML: efficient memory bandwidth management for ensuring QoS and improving server utilization"],"prefix":"10.1007","volume":"24","author":[{"given":"Hanul","family":"Sung","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeesoo","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghun","family":"Koo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeonsang","family":"Eom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"3191_CR1","unstructured":"Intel Performance Counter Monitor. https:\/\/software.intel.com\/en-us\/articles\/intel-performance-counter-monitor"},{"key":"3191_CR2","unstructured":"STREAM Benchmark. http:\/\/www.cs.virginia.edu\/stream\/ref.html"},{"key":"3191_CR3","unstructured":"Amy Ousterhout, J.B., Joshua\u00a0Fried, A.B., Hari\u00a0Balakrishnan, M.C.: Shenango: achieving high CPU efficiency for latency-sensitive datacenter workloads. In: Proceedings of the 16th USENIX Conference on Networked Systems Design and Implementation (2019)"},{"key":"3191_CR4","unstructured":"Azimi, R., Kwon, Y., Elnikety, S.,\u00a0Syamala, M.,\u00a0Narasayya, V.,\u00a0Herodotou, H., Microsoft, P.T., Alex, B., Microsoft, C.,\u00a0Jack, B., \u00a0Microsoft, Z., Wang, B.J., \u00a0Bing, M.: PerfIso: Performance Isolation for Commercial Latency-Sensitive Services C: alin Iorgulescu* EPFL. Technical report (2018)"},{"key":"3191_CR5","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01741-4","volume-title":"The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines","author":"LA Barroso","year":"2013","unstructured":"Barroso, L.A., Clidaras, J., Hoelzle, U.: The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. Morgan & Claypool Publishers, San Rafael (2013)"},{"key":"3191_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Z., Leng, J., Li, C., Zheng, W., Guo Avalon, M.: Towards QoS awareness and improved utilization through multi-resource management in datacenters. In: Proceedings of the International Conference on Supercomputing, pp. 272\u2013283, New York, NY, USA, Jun 2019. Association for Computing Machinery","DOI":"10.1145\/3330345.3330370"},{"key":"3191_CR7","doi-asserted-by":"crossref","unstructured":"Dauwe, D.,\u00a0Jonardi, E., \u00a0Friese, R., \u00a0Pasricha, 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, May 2015","DOI":"10.1109\/IPDPSW.2015.38"},{"key":"3191_CR8","unstructured":"Delimitrou, C., \u00a0Kozyrakis, C.: Quasar: Resource-efficient and qos-aware cluster management. In: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS \u201914, pp. 127\u2013144, New York, NY, USA (2014). ACM"},{"key":"3191_CR9","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-319-61756-5","volume-title":"Job Scheduling Strategies for Parallel Processing","author":"N Desai","year":"2017","unstructured":"Desai, N., Cirne, W.: Job Scheduling Strategies for Parallel Processing, pp. 274\u2013278. Springer, Cham (2017)"},{"key":"3191_CR10","doi-asserted-by":"crossref","unstructured":"Di, S., Kondo, D.,\u00a0Cirne, W.: Characterization and comparison of cloud versus grid workloads. In: 2012 IEEE International Conference on Cluster Computing, pp. 230\u2013238, Sept 2012","DOI":"10.1109\/CLUSTER.2012.35"},{"key":"3191_CR11","unstructured":"Dwyer, T.\u00a0,\u00a0Fedorova, A., \u00a0Blagodurov, S., Roth, M.,\u00a0Gaud, F., \u00a0Pei, J.: A practical method for estimating performance degradation on multicore processors, and its application to hpc workloads. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC \u201912, pp. 83:1\u201383:11, Los Alamitos, CA, USA, 2012. IEEE Computer Society Press"},{"key":"3191_CR12","doi-asserted-by":"crossref","unstructured":"Huang, S., \u00a0Huang, J.,\u00a0Dai, J., \u00a0Xie, T., \u00a0Huang, B.: The hibench benchmark suite: characterization of the mapreduce-based data analysis. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010), pp. 41\u201351, March 2010","DOI":"10.1109\/ICDEW.2010.5452747"},{"key":"3191_CR13","unstructured":"Hurt, K., John, E.: Analysis of memory sensitive spec cpu2006 integer benchmarks for big data benchmarking. In: Proceedings of the 1st Workshop on Performance Analysis of Big Data Systems, PABS \u201915, pp. 11\u201316, New York, NY, USA (2015). ACM"},{"issue":"2","key":"3191_CR14","first-page":"438","volume":"2","author":"S Kalmegh","year":"2015","unstructured":"Kalmegh, S.: Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of indian news. Int. J. Innov. Sci. Eng. Technol 2(2), 438\u2013446 (2015)","journal-title":"Int. J. Innov. Sci. Eng. Technol"},{"key":"3191_CR15","unstructured":"Kasture, H.,\u00a0Sanchez, D.: Ubik: efficient cache sharing with strict qos for latency-critical workloads. In: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS \u201914, pp. 729\u2013742, New York, NY, USA (2014). ACM"},{"key":"3191_CR16","doi-asserted-by":"crossref","unstructured":"Kasture, H.,\u00a0Sanchez, D.: Tailbench: a benchmark suite and evaluation methodology for latency-critical applications. In: 2016 IEEE International Symposium on Workload Characterization (IISWC), pp. 1\u201310, Sept 2016","DOI":"10.1109\/IISWC.2016.7581261"},{"key":"3191_CR17","unstructured":"Kyungyoung, C., Park, R.C.: Cloud based u-healthcare network with QoS guarantee for mobile health service. In: Cluster Computing (2017)"},{"key":"3191_CR18","unstructured":"Lakshmi\u00a0Devasena, C.: Comparative analysis of random forest, rep tree and j48 classifiers for credit risk prediction. In: International Journal of Computer Applications (0975-8887), International Conference on Communication, Computing and Information Technology (ICCCMIT-2014) (2014)"},{"key":"3191_CR19","doi-asserted-by":"crossref","unstructured":"Li Chunlin, T.J., \u00a0Youlong, L.: Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud. In: Cluster Computing (2017)","DOI":"10.1007\/s10586-017-1171-2"},{"key":"3191_CR20","unstructured":"Lo, D., \u00a0Cheng, L.,\u00a0Govindaraju, R., \u00a0Ranganathan, P.,\u00a0Kozyrakis, C.: Heracles: Improving resource efficiency at scale. In: Proceedings of the 42nd Annual International Symposium on Computer Architecture, ISCA \u201915, pp. 450\u2013462, New York, NY, USA (2015). ACM"},{"key":"3191_CR21","unstructured":"Mahmoud, Z.H.A., Badawy, M.,\u00a0Ali, H.A.: QoS provisioning framework for service-oriented internet of things (IoT). In: Cluster Computing (2019)"},{"key":"3191_CR22","unstructured":"Mars, J., \u00a0Tang, L., \u00a0Hundt, R.,\u00a0Skadron, K., Soffa, M.L.: Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations. In: Proceedings of the 44th Annual IEEE\/ACM International Symposium on Microarchitecture, MICRO-44, pp. 248\u2013259, New York, NY, USA (2011). ACM"},{"key":"3191_CR23","doi-asserted-by":"crossref","unstructured":"Anithadevi, N.,\u00a0Sundarambal, M.: A design of intelligent QoS aware web service recommendation system. In: Cluster Computing (2018)","DOI":"10.1007\/s10586-018-2279-8"},{"key":"3191_CR24","unstructured":"Nathuji, R., \u00a0Kansal, A., \u00a0Ghaffarkhah, A.: Q-clouds: managing performance interference effects for qos-aware clouds. In: Proceedings of the 5th European Conference on Computer Systems, EuroSys \u201910, pp. 237\u2013250, New York, NY, USA (2010). ACM"},{"key":"3191_CR25","doi-asserted-by":"crossref","unstructured":"Patel, T., \u00a0Tiwari, D.: CLITE: efficient and QoS-aware co-location of multiple latency-critical jobs for warehouse scale computers. In Proceedings\u20142020 IEEE International Symposium on High Performance Computer Architecture, HPCA 2020, pp. 193\u2013206. Institute of Electrical and Electronics Engineers Inc., Feb 2020","DOI":"10.1109\/HPCA47549.2020.00025"},{"key":"3191_CR26","doi-asserted-by":"crossref","unstructured":"Santiago Felici-Castell, J.S.G., Garcia-Pineda, M.: Adaptive QoE-based architecture on cloud mobile media for live streaming. In: Cluster Computing (2018)","DOI":"10.1007\/s10586-018-2876-6"},{"key":"3191_CR27","unstructured":"Sukhpal Singh Gill, M.S., Charana, I., Buyya, R.: CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing. In: Cluster Computing (2017)"},{"key":"3191_CR28","doi-asserted-by":"crossref","unstructured":"Sung, H., Min, J., \u00a0Ha, S., \u00a0Eom, H.: OMBM: optimized memory bandwidth management for ensuring QoS and high server utilization. In: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W), pp. 269\u2013276. IEEE, Sep 2017","DOI":"10.1109\/FAS-W.2017.158"},{"key":"3191_CR29","doi-asserted-by":"crossref","unstructured":"Witten, I., \u00a0Frank, E., Hall, M.\u00a0A., Pal, C.\u00a0J.: Data Mining: Practical Machine Learning Tools and Techniques (2016)","DOI":"10.1016\/B978-0-12-804291-5.00010-6"},{"key":"3191_CR30","doi-asserted-by":"crossref","unstructured":"Xu, C., \u00a0Felter, W., \u00a0Rajamani, K., Rubio, J.,\u00a0Ferreira, A., \u00a0Li, Y.: dCat: dynamic cache management for efficient, performance-sensitive infrastructure-as-a-service. In: Proceedings of the 13th EuroSys Conference, EuroSys 2018, volume 2018-January, pp. 1\u201313, New York, NY, USA, Apr 2018. Association for Computing Machinery, Inc.","DOI":"10.1145\/3190508.3190555"},{"key":"3191_CR31","unstructured":"Yang, H., \u00a0Breslow, A., \u00a0Mars, J., \u00a0Tang, L.: Bubble-flux: Precise online qos management for increased utilization in warehouse scale computers. In: Proceedings of the 40th Annual International Symposium on Computer Architecture, ISCA\u201913, pp. 607\u2013618, New York, NY, USA (2013). ACM"},{"key":"3191_CR32","unstructured":"Yang, X., Blackburn, S.\u00a0M., McKinley, K.\u00a0S.: Elfen scheduling: Fine-grain principled borrowing from latency-critical workloads using simultaneous multithreading. In: 2016 USENIX Annual Technical Conference (USENIX ATC 16), pp. 309\u2013322, Denver, CO, 2016. USENIX Association"},{"key":"3191_CR33","unstructured":"Yongfeng Cui, Y.\u00a0M., Zhongyuan\u00a0Zhao, \u00a0Dong, S.: Resource allocation algorithm design of high quality of service based on chaotic neural network in wireless communication technology. In: Cluster Computing (2017)"},{"key":"3191_CR34","unstructured":"Yun, H.,\u00a0Yao, G.,\u00a0Pellizzoni, R.,\u00a0Caccamo, M., \u00a0Sha, L.: Memguard: memory bandwidth reservation system for efficient performance isolation in multi-core platforms. In: 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 55\u201364, April, 2013"},{"key":"3191_CR35","unstructured":"Zhang, W., \u00a0Cui, W., \u00a0Fu, K., \u00a0Chen, Q., Mawhirter, D.\u00a0E., \u00a0Wu, B.,\u00a0Li, C., Guo, M.: Laius: towards latency awareness and improved utilization of spatial multitasking accelerators in datacenters. In: Proceedings of the International Conference on Supercomputing, pages 58\u201368. Association for Computing Machinery, Jun, 2019"},{"key":"3191_CR36","unstructured":"Zhu, H.,\u00a0Erez, M.: Dirigent: enforcing qos for latency-critical tasks on shared multicore systems. In: Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS \u201916, pp. 33\u201347, New York, NY, USA (2016). ACM"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-020-03191-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10586-020-03191-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-020-03191-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T05:17:43Z","timestamp":1670995063000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10586-020-03191-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["3191"],"URL":"https:\/\/doi.org\/10.1007\/s10586-020-03191-2","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2021,2,1]]},"assertion":[{"value":"2 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}