{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:10:01Z","timestamp":1780391401670,"version":"3.54.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030989774","type":"print"},{"value":"9783030989781","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-98978-1_4","type":"book-chapter","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T17:05:42Z","timestamp":1647968742000},"page":"48-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cross Inference of Throughput Profiles Using Micro Kernel Network Method"],"prefix":"10.1007","author":[{"given":"Nageswara S. V.","family":"Rao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anees","family":"Al-Najjar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neena","family":"Imam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengchun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajkumar","family":"Kettimuthu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian","family":"Foster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"4_CR1","unstructured":"Energy sciences network. http:\/\/www.es.net"},{"key":"4_CR2","unstructured":"Experimental physics and industrial control system. epics.anl.gov"},{"key":"4_CR3","unstructured":"Lab5: Setting WAN bandwidth with token bucket filter (2019). https:\/\/bit.ly\/3vhkdot"},{"key":"4_CR4","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511624216","volume-title":"Neural Network Learning: Theoretical Foundations","author":"M Anthony","year":"1999","unstructured":"Anthony, M., Bartlett, P.L.: Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge (1999)"},{"key":"4_CR5","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719437","volume-title":"Generalized Concavity","author":"M Avriel","year":"2010","unstructured":"Avriel, M., Diewert, W.E., Schaible, S., Zang, I.: Generalized Concavity. SIAM, Philadelphia (2010)"},{"issue":"5","key":"4_CR6","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/3012426.3022184","volume":"14","author":"N Cardwell","year":"2016","unstructured":"Cardwell, N., Cheng, Y., Gunn, C.S., Yeganeh, S.H., Jacobson, V.: BBR: congestion based congestion control. ACM Queue 14(5), 20\u201353 (2016)","journal-title":"ACM Queue"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Al-Najjar, A., et al.: Virtual framework for development and testing of federation software stack. In: 2021 IEEE 46th Conference on Local Computer Networks (LCN), pp. 323\u2013326. IEEE (2021)","DOI":"10.1109\/LCN52139.2021.9524993"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Floyd, S.: Highspeed TCP for large congestion windows. Internet draft, February 2003","DOI":"10.17487\/rfc3649"},{"key":"4_CR9","unstructured":"iMars3D: Preprocessing and reconstruction for the Neutron Imaging Beam Lines. https:\/\/github.com\/ornlneutronimaging\/iMars3D.git"},{"issue":"2","key":"4_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1145\/956981.956989","volume":"33","author":"T Kelly","year":"2003","unstructured":"Kelly, T.: Scalable TCP: improving performance in high speed wide area networks. Comput. Commun. Rev. 33(2), 83\u201391 (2003)","journal-title":"Comput. Commun. Rev."},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Phanekham, D., Nair, S., Rao, N.S.V., Truty, M.: Predicting throughput of cloud network infrastructure using neural networks. In: Workshop on Intelligent Cloud Computing and Networking (2021)","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484520"},{"key":"4_CR12","series-title":"Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-12971-2_2","volume-title":"Testbeds and Research Infrastructures for the Development of Networks and Communications","author":"NSV Rao","year":"2019","unstructured":"Rao, N.S.V., Liu, Q., Liu, Z., Kettimuthu, R., Foster, I.: Throughput analytics of data transfer infrastructures. In: Gao, H., Yin, Y., Yang, X., Miao, H. (eds.) TridentCom 2018. LNICST, vol. 270, pp. 20\u201340. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12971-2_2"},{"key":"4_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-19945-6_1","volume-title":"Machine Learning for Networking","author":"NSV Rao","year":"2019","unstructured":"Rao, N.S.V., Sen, S., Liu, Z., Kettimuthu, R., Foster, I.: Learning concave-convex profiles of data transport over dedicated connections. In: Renault, \u00c9., M\u00fchlethaler, P., Boumerdassi, S. (eds.) MLN 2018. LNCS, vol. 11407, pp. 1\u201322. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-19945-6_1"},{"key":"4_CR14","unstructured":"Rhee, I., Xu, L.: CUBIC: a new TCP-friendly high-speed TCP variant. In: 3rd International Workshop on Protocols for Fast Long-Distance Networks (2005)"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Rong, R., Liu, J.: Distributed mininet with symbiosis. In: International Conference on Communications, pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ICC.2017.7996343"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Settlemyer, B.W., Rao, N.S.V., Poole, S.W., Hodson, S.W., Hicks, S.E., Newman, P.M.: Experimental analysis of 10 gbps transfers over physical and emulated dedicated connections. In: International Conference on Computing, Networking and Communications (2012)","DOI":"10.1109\/ICCNC.2012.6167544"},{"key":"4_CR17","unstructured":"Shorten, R.N., Leith, D.J.: H-TCP: TCP for high-speed and long-distance networks. In: 3rd International Workshop on Protocols for Fast Long-Distance Networks (2004)"},{"key":"4_CR18","unstructured":"https:\/\/fasterdata.es.net\/host-tuning\/background\/"},{"key":"4_CR19","volume-title":"Statistical Learning Theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Networking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-98978-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T17:06:51Z","timestamp":1647968811000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-98978-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030989774","9783030989781"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-98978-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mln2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adda-association.org\/mln-2021\/Home.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"10","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":"33% - 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":"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)"}}]}}