{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:39:14Z","timestamp":1774021154156,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A2C2014333"],"award-info":[{"award-number":["2021R1A2C2014333"]}]},{"DOI":"10.13039\/501100003725","name":"Korean Government (MSIT)","doi-asserted-by":"publisher","award":["2021R1A2C2014333"],"award-info":[{"award-number":["2021R1A2C2014333"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra\u2019s algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.<\/jats:p>","DOI":"10.3390\/s23239608","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T07:59:31Z","timestamp":1701676771000},"page":"9608","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2416-2322","authenticated-orcid":false,"given":"Sujitha","family":"Venkatapathy","sequence":"first","affiliation":[{"name":"TIFAC-CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3761-9360","authenticated-orcid":false,"given":"Thiruvenkadam","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India"}]},{"given":"Han-Gue","family":"Jo","sequence":"additional","affiliation":[{"name":"School of Software, Kunsan National University, Gunsan 54150, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3936-1116","authenticated-orcid":false,"given":"In-Ho","family":"Ra","sequence":"additional","affiliation":[{"name":"School of Software, Kunsan National University, Gunsan 54150, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MWC.001.2100318","article-title":"Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions","volume":"29","author":"Babbar","year":"2022","journal-title":"IEEE Wirel. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39123","DOI":"10.1109\/ACCESS.2023.3267985","article-title":"Machine Learning in Network Slicing\u2014A Survey","volume":"11","author":"Phyu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MWC.2019.1800234","article-title":"An overview of network slicing for 5G","volume":"26","author":"Zhang","year":"2019","journal-title":"IEEE Wirel. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MCOM.2017.1600935","article-title":"Network slicing for 5G with SDN\/NFV: Concepts, architectures, and challenges","volume":"55","author":"Ameigeiras","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_5","unstructured":"Li, Q., Wu, G., Papathanassiou, A., and Mukherjee, U. (2016). An end-to-end network slicing framework for 5G wireless communication systems. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Irawan, D., Syambas, N.R., Ananda Kusuma, A.A.N., and Mulyana, E. (2020, January 4\u20135). Network Slicing Algorithms Case Study: Virtual Network Embedding. Proceedings of the 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA), Bandung, Indonesia.","DOI":"10.1109\/TSSA51342.2020.9310856"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"30331","DOI":"10.1109\/ACCESS.2019.2902432","article-title":"Optimizing network slice dimensioning via resource pricing","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TMC.2019.2952354","article-title":"Joint resource allocation for device-to-device communication assisted fog computing","volume":"20","author":"Yi","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TMC.2020.3026194","article-title":"A queueing game based management framework for fog computing with strategic computing speed control","volume":"21","author":"Yi","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/TMC.2019.2891736","article-title":"A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications","volume":"19","author":"Yi","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TMC.2019.2896950","article-title":"A machine learning approach to 5G infrastructure market optimization","volume":"19","author":"Bega","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Preciado-Velasco, J.E., Gonzalez-Franco, J.D., Anias-Calderon, C.E., Nieto-Hipolito, J.I., and Rivera-Rodriguez, R. (2021). 5G\/B5G Service Classification Using Supervised Learning. Appl. Sci., 11.","DOI":"10.3390\/app11114942"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s11276-015-0983-3","article-title":"Privacy-preserving data aggregation scheme against internal attackers in smart grids","volume":"22","author":"He","year":"2016","journal-title":"Wirel. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4285","DOI":"10.1109\/TVT.2020.2973294","article-title":"Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks","volume":"69","author":"Pham","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., and Srivastava, G. (2020). Deep neural networks to predict diabetic retinopathy. J. Ambient. Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-020-01963-7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1109\/TII.2017.2771382","article-title":"Certificateless Public Key Authenticated Encryption With Keyword Search for Industrial Internet of Things","volume":"14","author":"He","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1109\/JSAC.2019.2927065","article-title":"5G-Slicing-Enabled Scalable SDN Core Network: Toward an Ultra-Low Latency of Autonomous Driving Service","volume":"37","author":"Chekired","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103518","DOI":"10.1016\/j.csi.2021.103518","article-title":"Optimal 5G network slicing using machine learning and deep learning concepts","volume":"76","author":"Abidi","year":"2021","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Zhang, Q., Liu, F., Wang, J., Zhao, M., Zhang, Z., and Zhang, J. (2019, January 24\u201325). NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning. Proceedings of the 2019 IEEE\/ACM 27th International Symposium on Quality of Service (IWQoS), Phoenix, AZ, USA.","DOI":"10.1145\/3326285.3329056"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ins.2019.05.012","article-title":"Data-driven dynamic resource scheduling for network slicing: A Deep reinforcement learning approach","volume":"498","author":"Wang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gupta, R.K., and Misra, R. (2019, January 20\u201321). Machine Learning-based Slice allocation Algorithms in 5G Networks. Proceedings of the 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), Mumbai, India.","DOI":"10.1109\/ICAC347590.2019.9036741"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Abbas, K., Afaq, M., Khan, T.A., Mehmood, A., and Song, W.C. (2020, January 22\u201325). IBNSlicing: Intent-based network slicing framework for 5G networks using deep learning. Proceedings of the 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS), Daegu, Republic of Korea.","DOI":"10.23919\/APNOMS50412.2020.9237008"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thantharate, A., Paropkari, R., Walunj, V., and Beard, C. (2019, January 10\u201312). Deepslice: A deep learning approach towards an efficient and reliable network slicing in 5G networks. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993066"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TNET.2021.3080197","article-title":"Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice","volume":"29","author":"Zhang","year":"2021","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"392","DOI":"10.23919\/JCN.2023.000019","article-title":"E2E network slice management framework for 5G multi-tenant networks","volume":"25","author":"Wang","year":"2023","journal-title":"J. Commun. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/MCOM.2017.1600947","article-title":"PERMIT: Network Slicing for Personalized 5G Mobile Telecommunications","volume":"55","author":"Taleb","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/JCN.2020.000026","article-title":"5G network slices embedding with sharable virtual network functions","volume":"22","author":"Mei","year":"2020","journal-title":"J. Commun. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"150517","DOI":"10.1109\/ACCESS.2019.2947454","article-title":"Efficient and secure 5G core network slice provisioning based on VIKOR approach","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1145\/1971162.1971168","article-title":"Virtual Network Embedding through Topology-Aware Node Ranking","volume":"41","author":"Cheng","year":"2011","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19691","DOI":"10.1109\/ACCESS.2018.2822398","article-title":"A Service-Oriented Deployment Policy of End-to-End Network Slicing Based on Complex Network Theory","volume":"6","author":"Guan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","unstructured":"Raghavendra Prasad, J., Senthil, M., Yadav, A., Gupta, P., and Anusha, K. (2021). Inventive Communication and Computational Technologies: Proceedings of ICICCT 2020, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Archanaa, R., Athulya, V., Rajasundari, T., and Kiran, M.V.K. (2017, January 6\u20137). A comparative performance analysis on network traffic classification using supervised learning algorithms. Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, Tamil Nadu, India.","DOI":"10.1109\/ICACCS.2017.8014634"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/978-981-16-3945-6_50","article-title":"Vpn network traffic classification using entropy estimation and time-related features","volume":"Volume 2","author":"Balachandran","year":"2022","journal-title":"IOT with Smart Systems: Proceedings of ICTIS 2021"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.procs.2017.09.089","article-title":"Usage and analysis of Twitter during 2015 Chennai flood towards disaster management","volume":"115","author":"Nair","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Landro, N., Gallo, I., and La Grassa, R. (2021). Combining Optimization Methods Using an Adaptive Meta Optimizer. Algorithms, 14.","DOI":"10.3390\/a14060186"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of Scaling in Random Networks","volume":"286","author":"Barabasi","year":"1999","journal-title":"Science"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Thiruvenkadam, S., Sujitha, V., Jo, H.G., and Ra, I.H. (2022). A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding. Appl. Sci., 12.","DOI":"10.3390\/app12146942"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/1355734.1355737","article-title":"Rethinking virtual network embedding: Substrate support for path splitting and migration","volume":"38","author":"Yu","year":"2008","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_39","unstructured":"Wang, Z., Han, Y., Lin, T., Tang, H., and Ci, S. (2012, January 3\u20137). Virtual network embedding by exploiting topological information. Proceedings of the 2012 IEEE Global Communications Conference (GLOBECOM), Anaheim, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9608\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:41Z","timestamp":1760132261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239608"],"URL":"https:\/\/doi.org\/10.3390\/s23239608","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]}}}