{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:59:28Z","timestamp":1758268768595},"reference-count":44,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2019,5,1]]},"DOI":"10.1587\/transinf.2018ntp0016","type":"journal-article","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T18:22:51Z","timestamp":1556648571000},"page":"898-909","source":"Crossref","is-referenced-by-count":10,"title":["Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Abu Hena Al","family":"MUKTADIR","sequence":"first","affiliation":[{"name":"National Institute of Information and Communications Technology (NICT)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takaya","family":"MIYAZAWA","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology (NICT)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"MARTINEZ-JULIA","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology (NICT)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroaki","family":"HARAI","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology (NICT)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ved P.","family":"KAFLE","sequence":"additional","affiliation":[{"name":"National Institute of Information and Communications Technology (NICT)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] J. Ordonez-Lucena, P. Ameigeiras, D. Lopez, J.J. Ramos-Munoz, J. Lorca, and J. Folgueira, \u201cNetwork slicing for 5G with SDN\/NFV: Concepts, architectures, and challenges,\u201d IEEE Commun. Mag., vol.55, no.5, pp.80-87, May 2017. 10.1109\/mcom.2017.1600935","DOI":"10.1109\/MCOM.2017.1600935"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] K. Fujikawa, V.P. Kafle, P. Martinez-Julia, A.H.A. Muktadir, and H. Harai, \u201cAutomatic construction of name-bound virtual networks for IoT,\u201d Proc. 41st IEEE COMPSAC, pp.529-537, July 2017. 10.1109\/compsac.2017.71","DOI":"10.1109\/COMPSAC.2017.71"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] J. Sim\u00e3o and L. Veiga, \u201cPartial utility-driven scheduling for flexible SLA and pricing arbitration in clouds,\u201d IEEE Trans. Cloud Comput., vol.4, no.4, pp.467-480, Oct. 2016. 10.1109\/tcc.2014.2372753","DOI":"10.1109\/TCC.2014.2372753"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] M. Aazam and E.-N. Huh, \u201cDynamic resource provisioning through Fog micro datacenter,\u201d Proc. IEEE PerCOM, pp.105-110, March 2015. 10.1109\/percomw.2015.7134002","DOI":"10.1109\/PERCOMW.2015.7134002"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Shen, V.V. Beek, and A. Iosup, \u201cStatistical characterization of business-critical workloads hosted in cloud datacenters,\u201d Proc. 15th IEEE\/ACM CCGrid, pp.465-474, May 2015. 10.1109\/ccgrid.2015.60","DOI":"10.1109\/CCGrid.2015.60"},{"key":"6","unstructured":"[6] Amazon Elastic Compute Cloud (EC2) Instance Types, https:\/\/aws.amazon.com\/ec2\/instance-types\/."},{"key":"7","unstructured":"[7] Google compute engine, https:\/\/cloud.google.com\/compute\/."},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] S. Shen, K. Deng, A. Iosup, and D. Epema, Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances, pp.242-254, Springer: Berlin Heidelberg, 2013.","DOI":"10.1007\/978-3-642-40047-6_27"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] M. Melo, S. Sargento, U. Killat, A. Timm-Giel, and J.Carapinha, \u201cOptimal virtual network embedding: Node-link formulation,\u201d IEEE Trans. Netw. Service Manag., vol.10, no.4, pp.356-368, Dec. 2013. 10.1109\/tnsm.2013.092813.130397","DOI":"10.1109\/TNSM.2013.092813.130397"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] R. Yu, G. Xue, and X. Zhang, \u201cQos-aware and reliable traffic steering for service function chaining in mobile networks,\u201d IEEE J. Sel. Areas Commun., vol.35, no.11, pp.2522-2531, Nov. 2017. 10.1109\/jsac.2017.2760158","DOI":"10.1109\/JSAC.2017.2760158"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J.F. Botero, X. Hesselbach, M. Duelli, D. Schlosser, A. Fischer, and H. de Meer, \u201cEnergy efficient virtual network embedding,\u201d IEEE Commun. Lett., vol.16, no.5, pp.756-759, May 2012. 10.1109\/lcomm.2012.030912.120082","DOI":"10.1109\/LCOMM.2012.030912.120082"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] T. Miyazawa and H. Harai, \u201cSupervised learning based automatic adaptation of virtualized resource selection policy,\u201d Proc. 17th IEEE Networks, pp.170-175, Sept. 2016. 10.1109\/netwks.2016.7751171","DOI":"10.1109\/NETWKS.2016.7751171"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] R. Mijumbi, J.-L. Gorricho, J. Serrat, M. Claeys, F.D. Turck, and S. Latr\u00e9, \u201cDesign and evaluation of learning algorithms for dynamic resource management in virtual networks,\u201d Proc. IEEE NOMS, pp.1-9, May 2014. 10.1109\/noms.2014.6838258","DOI":"10.1109\/NOMS.2014.6838258"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Rahman, T. Ahmed, M. Huynh, M. Tornatore, and B. Mukherjee, \u201cAuto-scaling VNFs using machine learning to improve QoS and reduce cost,\u201d Proc. IEEE ICC, pp.1-6, May 2018. 10.1109\/icc.2018.8422788","DOI":"10.1109\/ICC.2018.8422788"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] R. Mijumbi, S. Hasija, S. Davy, A. Davy, B. Jennings, and R. Boutaba, \u201cTopology-aware prediction of virtual network function resource requirements,\u201d IEEE Trans. Netw. Service Manag., vol.14, no.1, pp.106-120, March 2017. 10.1109\/tnsm.2017.2666781","DOI":"10.1109\/TNSM.2017.2666781"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] Y. Jiang, C.-S. Perng, T. Li, and R.N. Chang, \u201cCloud analytics for capacity planning and instant VM provisioning,\u201d IEEE Trans. Netw. Service Manag., vol.10, no.3, pp.312-325, Sept. 2013. 10.1109\/tnsm.2013.051913.120278","DOI":"10.1109\/TNSM.2013.051913.120278"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] G. Zhang, X. Zhu, W. Bao, H. Yan, and D. Tan, \u201cLocal storage-based consolidation with resource demand prediction and live migration in clouds,\u201d IEEE Access, vol.6, pp.26,854-26,865, April 2018. 10.1109\/access.2018.2825354","DOI":"10.1109\/ACCESS.2018.2825354"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J.B. Wang, J. Wang, Y. Wu, J.Y. Wang, H. Zhu, M. Lin, and J. Wang, \u201cA machine learning framework for resource allocation assisted by cloud computing,\u201d IEEE Netw., vol.32, no.2, pp.144-151, March 2018.","DOI":"10.1109\/MNET.2018.1700293"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] L. Xu, Z. Zeng, and X. Ye, \u201cMulti-objective optimization based virtual resource allocation strategy for cloud computing,\u201d Proc. 11th IEEE\/ACIS ICIS, pp.56-61, May 2012. 10.1109\/icis.2012.74","DOI":"10.1109\/ICIS.2012.74"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] N. Kato, Z.M. Fadlullah, B. Mao, F. Tang, O. Akashi, T. Inoue, and K. Mizutani, \u201cThe deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective,\u201d IEEE Wireless Commun., vol.24, no.3, pp.146-153, June 2017. 10.1109\/mwc.2016.1600317wc","DOI":"10.1109\/MWC.2016.1600317WC"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, \u201cMachine learning paradigms for next-generation wireless networks,\u201d IEEE Wireless Commun., vol.24, no.2, pp.98-105, April 2017. 10.1109\/mwc.2016.1500356wc","DOI":"10.1109\/MWC.2016.1500356WC"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H. Zhang, \u201cIntelligent 5G: When cellular networks meet artificial intelligence,\u201d IEEE Wireless Commun., vol.24, no.5, pp.175-183, Oct. 2017. 10.1109\/mwc.2017.1600304wc","DOI":"10.1109\/MWC.2017.1600304WC"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Luo, W. Song, and L. Yin, \u201cReliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in industrial cloud,\u201d IEEE Access, vol.6, pp.23,043-23,052, March 2018. 10.1109\/access.2018.2816983","DOI":"10.1109\/ACCESS.2018.2816983"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] H.A. Alameddine, S. Sebbah, and C. Assi, \u201cOn the Interplay Between Network Function Mapping and Scheduling in VNF-Based Networks: A Column Generation Approach,\u201d IEEE Trans. Netw. and Service Manag., vol.14, no.4, pp.860-874, Dec. 2017. 10.1109\/tnsm.2017.2757266","DOI":"10.1109\/TNSM.2017.2757266"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] Y. Xu and V.P. Kafle, \u201cA Delay-Aware Service Function Chain Placement Scheme Based on Dynamic Programming,\u201d Proc. IEEE LANMAN, pp.110-111, June 2018. 10.1109\/lanman.2018.8475115","DOI":"10.1109\/LANMAN.2018.8475115"},{"key":"26","unstructured":"[26] D. Crankshaw, X. Wang, G. Zhou, M.J. Franklin, J.E. Gonzalez, and I. Stoica, \u201cClipper: A low-latency online prediction serving system,\u201d Proc. 14th USENIX NSDI, pp.613-627, April 2017."},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] L. Chen and H. Shen, \u201cConsidering resource demand misalignments to reduce resource over-provisioning in cloud datacenters,\u201d Proc. IEEE INFOCOM, pp.1-9, May 2017. 10.1109\/infocom.2017.8057084","DOI":"10.1109\/INFOCOM.2017.8057084"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] I. Houidi, W. Louati, D. Zeghlache, and S. Baucke, \u201cVirtual resource description and clustering for virtual network discovery,\u201d Proc. IEEE ICC Workshops, pp.1-6, June 2009. 10.1109\/iccw.2009.5207979","DOI":"10.1109\/ICCW.2009.5207979"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Y. Wang, Q. He, X. Zhang, D. Ye, and Y. Yang, \u201cEfficient QoS-aware service recommendation for multi-tenant service-based systems in cloud,\u201d IEEE Trans. Services Comput., Online, Oct. 2017.","DOI":"10.1109\/TSC.2017.2761346"},{"key":"30","unstructured":"[30] J. Read, \u201cScalable multi-label classification,\u201d Ph.D. dissertation, University of Waikato, Hamilton, New Zealand, Sept. 2010."},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] C. Bielza, G. Li, and P. Larra\u00f1aga, \u201cMulti-dimensional classification with Bayesian networks,\u201d Int. J. Approx. Reasoning, vol.52, no.6, pp.705-727, Sept. 2011. 10.1016\/j.ijar.2011.01.007","DOI":"10.1016\/j.ijar.2011.01.007"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] R. Polikar, \u201cEnsemble based systems in decision making,\u201d IEEE Circuits Syst. Mag., vol.6, no.3, pp.21-45, Sept. 2006. 10.1109\/mcas.2006.1688199","DOI":"10.1109\/MCAS.2006.1688199"},{"key":"33","unstructured":"[33] R. Polikar, \u201cEnsemble learning,\u201d http:\/\/www.scholarpedia.org\/article\/Ensemble_learning\/."},{"key":"34","unstructured":"[34] Classifier of WEKA, http:\/\/weka.sourceforge.net\/doc.dev\/weka\/classifiers\/Classifier.html."},{"key":"35","unstructured":"[35] J. Read, P. Reutemann, B. Pfahringer, and G. Holmes, \u201cMEKA: A multi-label\/multi-target extension to WEKA,\u201d J Mach. Learning Research, vol.17, no.21, pp.1-5, 2016."},{"key":"36","unstructured":"[36] ARFF Format, https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/arff.html."},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten, \u201cThe WEKA data mining software: an update,\u201d SIGKDD Explor. Newsl., vol.11, no.1, pp.10-18, Nov. 2009. 10.1145\/1656274.1656278","DOI":"10.1145\/1656274.1656278"},{"key":"38","unstructured":"[38] The Grid Workloads Archive, http:\/\/gwa.ewi.tudelft.nl\/datasets\/gwa-t-12-bitbrains."},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] D. Dietrich, A. Rizk, and P. Papadimitriou, \u201cMulti-Provider Virtual Network Embedding With Limited Information Disclosure,\u201d IEEE Trans. Netw. and Service Manag., vol.12, no.2, pp.188-201, June 2015. 10.1109\/tnsm.2015.2417652","DOI":"10.1109\/TNSM.2015.2417652"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] J.C. Moore, H.R. Rao, and A.B. Whinston, \u201cMulti-agent resource allocation: an incomplete information perspective,\u201d IEEE Trans. Syst., Man, Cybern., vol.24, no.8, pp.1208-1219, Aug. 1994. 10.1109\/21.299702","DOI":"10.1109\/21.299702"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] G. Carrozza, L. Battaglia, V. Manetti, A. Marotta, R. Canonico, and S. Avallone, \u201cOn the Evaluation of VM Provisioning Time in Cloud Platforms for Mission-Critical Infrastructures,\u201d Proc. 14th IEEE\/ACM CCGrid Symp., Chicago, IL, pp.802-810, 2014. 10.1109\/ccgrid.2014.94","DOI":"10.1109\/CCGrid.2014.94"},{"key":"42","unstructured":"[42] ITU-R Rec. M.2083-0, \u201cIMT Vision Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond,\u201d Sept. 2015."},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] M. Wang, Y. Cui, X. Wang, S. Xiao, and J. Jiang, \u201cMachine learning for networking: Workflow, advances and opportunities,\u201d IEEE Netw., vol.32, no.2, pp.92-99, March 2018. 10.1109\/mnet.2017.1700200","DOI":"10.1109\/MNET.2017.1700200"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] K. Katsalis, N. Nikaein, E. Schiller, A. Ksentini, and T. Braun, \u201cNetwork slices toward 5G communications: Slicing the LTE network,\u201d IEEE Commun. Mag., vol.55, no.8, pp.146-154, Aug. 2017. 10.1109\/mcom.2017.1600936","DOI":"10.1109\/MCOM.2017.1600936"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/5\/E102.D_2018NTP0016\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,3]],"date-time":"2019-05-03T23:25:25Z","timestamp":1556925925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/5\/E102.D_2018NTP0016\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2018ntp0016","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]}}}