[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123638-en":3,"doc-seo-123638-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123638,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Enhancing Service Classification for Network Slicing in 5G Using Machine Learning Algorithms","Network Function Virtualization (NFV) enables network slicing in 5G, offering flexibility but creating significant management complexity. A slice comprises required services, potentially organized into sub-slices or service types. This research improves real-time availability and scalability by managing inter-slice and intra-slice performance, enhancing QoS for network resources and QoE for users. Machine learning is applied to classify services and predict accurate service delivery within NFV-based prototype slices, achieving up to 99.3% accuracy.","This is a peer-reviewed, post-print (final draft post-refereeing) version of the following published document and is licensed under All Rights Reserved license:  \nMohammedali, Noor Abdalkarem, Kanakis, Triantafyllos, AlSherbaz, Ali ORCID: 0000-0002-0995-1262, Agyeman, Michael Opoku and Hasson, Saad Talib (2023) Enhancing service classification for network slicing in 5G using machine learning algorithms . In: 6th International Conference, New Trends in Information and Communications Technology Applications, 16- 17 November 2022, Baghdad, Iraq . ISSN 1865-0929 ISBN 9783031354410  \nOfficial URL: [http://doi.org/10.1007/978-3-031-35442-7](http://doi.org/10.1007/978-3-031-35442-7)_2  \nDOI: [http://dx.doi.org/10.1007/978-3-031-35442-7_2](http://dx.doi.org/10.1007/978-3-031-35442-7_2)  \nEPrint URI: [https://eprints.glos.ac.uk/id/eprint/12955](https://eprints.glos.ac.uk/id/eprint/12955)  \nDisclaimer  \nThe University of Gloucestershire has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material.  \nThe University of Gloucestershire makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.  \nThe University of Gloucestershire makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.  \nThe University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.  \nPLEASE SCROLL DOWN FOR TEXT.  \nEnhancing Service Classification for Network Slicing in 5G Using Machine Learning Algorithms  \nNoor Abdalkarem Mohammedali1 , Triantafyllos Kanakis 1 , Ali Al-Sherbaz2 , Michael Opoku Agyeman 1 , and Saad Talib Hasson3  \n1 Centre for Smart and Advanced Technologies (CAST), University of Northampton, Northampton, [UK noor.mohammedali@northampton.ac.uk](UK noor.mohammedali@northampton.ac.uk)  \n2 School of Computing and Engineering, University of Gloucestershire, Gloucestershire, UK  \n3 College of Information Technology, University of Babylon, Babylon, Iraq  \nAbstract  \nIn a virtualization aspect, Network Function Virtualization (NFV) has a role in implementing network slicing. Using NFV to slice the network, make the network more flexible, but very complicated in term of management. A slice is a set of services that the network needs based on the user requirements. Moreover, each slice has a set of services called sub-slice, or one type of service. This research aims to improve the availability and scalability of the services in network slicing by managing the performance of the inter/intra slice in real-time. Also, this research will enhance the Quality of Service (QoS) for the network resources and services and the Quality of Experience (QoE) for the users within the slice when we applied machine learning algorithms to classify and predicate accurate service to the user. With this research, we implemented the slices based on the principles of NFV to deliver flexibility in the 5G network by creating multiple slices on top of the physical network. When the implementation of the prototype is completed, traffic generated tool was used to send traffic over the slices. After data collection, we classified different services using machine learning algorithms. The optimizable tree model had almost high accuracy among other algorithms which was 99.3% .  \nKeywords: 5G · Network Slicing · NSSF · Traffic-Classification · QoS · inter-slice · NFV · E2E · Machine Learning  \n1 Introduction  \nNext-generation networks will be configured with softwarization techniques based on a Software-Defined Network (SDN) and NFV. The implementation of the network elements will be started from the core layer to the acce","cbCaii85Wfz1pj7H","https://ap.wps.com/l/cbCaii85Wfz1pj7H","pdf",1464753,1,14,"English","en",105,"# Introduction\n## Background: SDN, NFV and softwarization\n## Network slicing concepts and resources\n## QoS/QoE standards and related work","[{\"question\":\"How does the research improve network slicing management in 5G?\",\"answer\":\"It manages inter-slice and intra-slice performance in real time to improve availability and scalability, while also enhancing QoS for resources and QoE for users.\"},{\"question\":\"What role does machine learning play in the proposed approach?\",\"answer\":\"Machine learning algorithms classify services and predicate accurate service to the user based on collected traffic data from the network slices.\"},{\"question\":\"What architecture principle is used to implement the slices?\",\"answer\":\"Slices are implemented based on NFV principles, creating multiple slices on top of the physical network to deliver flexibility in the 5G network.\"}]","Enhancing Service Classification for Network Slicing in 5G Using Machine Learning Algorithms | 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does the research improve network slicing management in 5G?","Question",{"text":75,"@type":76},"It manages inter-slice and intra-slice performance in real time to improve availability and scalability, while also enhancing QoS for resources and QoE for users.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does machine learning play in the proposed approach?",{"text":80,"@type":76},"Machine learning algorithms classify services and predicate accurate service to the user based on collected traffic data from the network slices.",{"name":82,"@type":73,"acceptedAnswer":83},"What architecture principle is used to implement the slices?",{"text":84,"@type":76},"Slices are implemented based on NFV principles, creating multiple slices on top of the physical network to deliver flexibility in the 5G 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