{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:57:33Z","timestamp":1743029853024,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030675363"},{"type":"electronic","value":"9783030675370"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67537-0_37","type":"book-chapter","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T13:12:57Z","timestamp":1611234777000},"page":"620-639","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Online Estimation of Thrashing-Avoiding Memory Reservations for Long-Lived Containers"],"prefix":"10.1007","author":[{"given":"Jiayun","family":"Lin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"issue":"7","key":"37_CR1","first-page":"1578","volume":"56","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Wang, L., Cheng, Y.: Performance optimization of Lustre file system based on reinforcement learning. J. Comput. Res. Dev. 56(7), 1578\u20131586 (2019)","journal-title":"J. Comput. Res. Dev."},{"issue":"7","key":"37_CR2","first-page":"1202","volume":"48","author":"T Zhao","year":"2011","unstructured":"Zhao, T., Dong, S., March, V., et al.: Predicting the parallel file system performance via machine learning. J. Comput. Res. Dev. 48(7), 1202\u20131215 (2011)","journal-title":"J. Comput. Res. Dev."},{"key":"37_CR3","unstructured":"Boutin, E., Ekanayake, J., Lin, W., et al.: Apollo: scalable and coordinated scheduling for cloud-scale computing. In: 11th Symposium on Operating Systems Design and Implementation (OSDI), Broomfield, CO, pp. 285\u2013300 (2014)"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, J., Guo, M., et al.: Bestconfig: tapping the performance potential of systems via automatic configuration tuning. In: 2017 Symposium on Cloud Computing (SoCC), Santa Clara, California, pp. 338\u2013350 (2017)","DOI":"10.1145\/3127479.3128605"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Vavilapalli, V.K., Murthy, A.C., Dougla, C., et al.: Apache Hadoop YARN: yet another resource negotiator. In: 4th Annual Symposium on Cloud Computing (SoCC), Santa Clara, California, no. 5, pp. 1\u201316 (2013)","DOI":"10.1145\/2523616.2523633"},{"key":"37_CR6","unstructured":"Hindman, B., Konwinski, A., Zaharia, M., et al.: Mesos: a platform for fine-grained resource sharing in the data center. In: 8th conference on Networked Systems Design and Implementation (NSDI), Boston, MA, pp. 295\u2013308 (2011)"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Schwarzkopf, M., Konwinski, A., Abd-El-Malek, M., et al.: Omega: flexible, scalable schedulers for large compute clusters. In: 8th ACM European Conference on Computer Systems (EuroSys), Prague, Czech Republic, pp. 351\u2013364 (2013)","DOI":"10.1145\/2465351.2465386"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Verma, A., Pedrosa, L., Korupolu, M., et al.: Large-scale cluster management at Google with Borg. In: 10th European Conference on Computer Systems (EuroSys), Bordeaux, France, no. 18, pp. 1\u201317 (2015)","DOI":"10.1145\/2741948.2741964"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Burns, B., Grant, B., Oppenheimer, D., et al.: Borg, Omega, and Kubernetes. In: Communications of the ACM, New York, USA, vol. 59, no. 5, pp. 50\u201357 (2016)","DOI":"10.1145\/2890784"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Xu, G., Xu, C.: MEER: online estimation of optimal memory reservations for long lived containers in in-memory cluster computing. In: 39th IEEE International Conference on Distributed Computing Systems (ICDCS), Dallas, TX, USA, pp. 23\u201334 (2019)","DOI":"10.1109\/ICDCS.2019.00012"},{"key":"37_CR11","unstructured":"Garefalakis, P., Karanasos, K., Pietzuch, P., et al.: Medea: scheduling of long running applications in shared production clusters. In: 13th EuroSys Conference, Porto, Portugal, no. 4, pp. 1\u201313 (2018)"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Chen, H., Jiang, G., Zhang, H., et al.: Boosting the performance of computing systems through adaptive configuration tuning. In: 2009 ACM Symposium on Applied Computing (SAC), Honolulu, Hawaii, pp. 1045\u20131049 (2009)","DOI":"10.1145\/1529282.1529511"},{"key":"37_CR13","unstructured":"Abadi, M., Barham, P., Chen, J., et al.: Tensorflow: a system for large-scale machine learning. In: 12th conference on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA, pp. 265\u2013283 (2016)"},{"issue":"1","key":"37_CR14","first-page":"1235","volume":"17","author":"X Meng","year":"2016","unstructured":"Meng, X., Bradley, J., Yavuz, B., et al.: Mllib: Machine learning in apache spark. J. Mach. Learn. Res. 17(1), 1235\u20131241 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"37_CR15","unstructured":"Zaharia, M., Chowdhury, M., Das, T., et al.: Resilient distributed datasets: a faulttolerant abstraction for in-memory cluster computing. In: 9th Conference on Networked Systems Design and Implementation (NSDI), San Jose, CA, p. 2 (2012)"},{"key":"37_CR16","unstructured":"Apache flink. http:\/\/flink.apache.org. Accessed 30 Mar 2020"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Toshniwal, A., Taneja, S., Shukla, A., et al.: Storm@twitter. In: 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, Utah, USA, pp. 147\u2013156 (2014)","DOI":"10.1145\/2588555.2595641"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Zaharia, M., Das, T., Li, H., et al.: Discretized streams: fault-tolerant streaming computation at scale. In: 24th ACM Symposium on Operating Systems Principles (SOSP), Farminton, Pennsylvania, pp. 423\u2013438 (2013)","DOI":"10.1145\/2517349.2522737"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Armbrust, M., Xin, R.S., Lian, C., et al.: Spark SQL: relational data processing in spark. In: 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, pp. 1383\u20131394 (2015)","DOI":"10.1145\/2723372.2742797"},{"key":"37_CR20","unstructured":"Kornacker, M., Behm, A., Bittorf, V., et al.: Impala: a modern, open-source SQL engine for hadoop. In: 7th Biennial Conference on Innovative Data Systems Research (CIDR) (2015)"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Saha, B., Shah, H., Seth, S., et al.: Apache Tez: a unifying framework for modeling and building data processing applications. In: 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, pp. 1357\u20131369 (2015)","DOI":"10.1145\/2723372.2742790"},{"key":"37_CR22","unstructured":"Gonzalez, J.E., Xin, R.S., Dave, A., et al.: Graphx: graph processing in a distributed dataflow framework. In: 11th Conference on Operating Systems Design and Implementation (OSDI), Broomfield, CO, pp. 599\u2013613 (2014)"},{"issue":"8","key":"37_CR23","doi-asserted-by":"publisher","first-page":"716","DOI":"10.14778\/2212351.2212354","volume":"5","author":"Y Low","year":"2012","unstructured":"Low, Y., Bickson, D., Gonzalez, J., et al.: Distributed graphlab: a framework for machine learning and data mining in the cloud. VLDB Endow. 5(8), 716\u2013727 (2012)","journal-title":"VLDB Endow."},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Malewicz, G., Austern, M.H., Bik, A.J.C., et al.: Pregel: a system for large-scale graph processing. In: 2010 ACM SIGMOD International Conference on Management of Data, Indianapolis, Indiana, USA, pp. 135\u2013146 (2010)","DOI":"10.1145\/1807167.1807184"},{"key":"37_CR25","unstructured":"Iorgulescu, C., Dinu, F., Raza, A., et al.: Don\u2019t cry over spilled records: memory elasticity of data-parallel applications and its application to cluster scheduling. In: Annual Technical Conference (ATC), pp. 97\u2013109 (2017)"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Herodotou, H., Dong, F., Babu, S.: No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics. In: 2nd ACM Symposium on Cloud Computing (SoCC), Cascais, Portugal, no. 18, pp. 1\u201314 (2011)","DOI":"10.1145\/2038916.2038934"},{"key":"37_CR27","unstructured":"Alipourfard, O., Liu, H.H., Chen, J., et al.: Cherrypick: adaptively unearthing the best cloud configurations for big data analytics. In: 14th Symposium on Networked Systems Design and Implementation (NSDI), Boston, MA, pp. 469\u2013482 (2017)"},{"issue":"6","key":"37_CR28","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/JSAC.2019.2904371","volume":"37","author":"NV Huynh","year":"2019","unstructured":"Huynh, N.V., Nguyen, D.N., Dutkiewicz, E.: Optimal and fast real-time resource slicing with deep dueling neural networks. IEEE J. Sel. Areas Commun. 37(6), 1455\u20131470 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Xu, G., Xu, C.: Prometheus: online estimation of optimal memory demands for workers in in-memory distributed computation. In: The ACM Symposium on Cloud Computing (SoCC), Santa Clara, California, p. 655 (2017)","DOI":"10.1145\/3127479.3132689"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Chen, W., Pi, A., Wang, S., et al.: Pufferfish: container-driven elastic memory management for data-intensive applications. In: the ACM Symposium on Cloud Computing (SoCC), Santa Cruz, CA, USA, pp. 259\u2013271 (2019)","DOI":"10.1145\/3357223.3362730"},{"key":"37_CR31","unstructured":"Klimovic, A., Litz, H., Kozyrakis, C.: Selecta: heterogeneous cloud storage configuration for data analytics. In: 2018 USENIX Annual Technical Conference (ATC), Boston, MA, USA, pp. 759\u2013773 (2018)"},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Liu, L., Xu, H.: Elasecutor: elastic executor scheduling in data analytics systems. In: The ACM Symposium on Cloud Computing (SoCC), Carlsbad, CA, USA, pp. 107\u2013120 (2018)","DOI":"10.1145\/3267809.3267818"},{"issue":"2","key":"37_CR33","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TSC.2016.2518161","volume":"11","author":"G Peng","year":"2018","unstructured":"Peng, G., Wang, H., Dong, J., et al.: Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment. IEEE Trans. Serv. Comput. (TSC) 11(2), 306\u2013317 (2018)","journal-title":"IEEE Trans. Serv. Comput. (TSC)"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Erradi, A., Iqbal, W., Mahmood, A., et al.: Web application resource requirements estimation based on the workload latent features. IEEE Trans. Serv. Comput. (TSC), 1(2019)","DOI":"10.1109\/TSC.2019.2918776"},{"key":"37_CR35","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.comcom.2019.12.028","volume":"151","author":"HA Kholidy","year":"2020","unstructured":"Kholidy, H.A.: An intelligent swarm based prediction approach for predicting clou computing user resource needs. Comput. Commun. (CC) 151, 133\u2013144 (2020)","journal-title":"Comput. Commun. (CC)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Collaborative Computing: Networking, Applications and Worksharing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67537-0_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T13:23:33Z","timestamp":1619270613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67537-0_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030675363","9783030675370"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67537-0_37","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CollaborateCom","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Collaborative Computing: Networking, Applications and Worksharing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"colcom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/collaboratecom.eai-conferences.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"61","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":"16","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":"29% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to COVID-19 pandemic.","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)"}}]}}