Performance Model for Video Service in 5G Networks
Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we prop...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/12/6/99 |
| _version_ | 1797565793318207488 |
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| author | Jiao Wang Jay Weitzen Oguz Bayat Volkan Sevindik Mingzhe Li |
| author_facet | Jiao Wang Jay Weitzen Oguz Bayat Volkan Sevindik Mingzhe Li |
| author_sort | Jiao Wang |
| collection | DOAJ |
| description | Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach. |
| first_indexed | 2024-03-10T19:18:01Z |
| format | Article |
| id | doaj.art-872d4780587c4103b915b5159087ecd4 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T19:18:01Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-872d4780587c4103b915b5159087ecd42023-11-20T03:13:46ZengMDPI AGFuture Internet1999-59032020-06-011269910.3390/fi12060099Performance Model for Video Service in 5G NetworksJiao Wang0Jay Weitzen1Oguz Bayat2Volkan Sevindik3Mingzhe Li4Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USADepartment of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USAGraduate School of Science and Engineering, Altinbas University, 34217 Istanbul, TurkeyDepartment of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USAQ Factor Communications, 255 Bear Hill Road, Waltham, MA 02451, USANetwork slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.https://www.mdpi.com/1999-5903/12/6/99interference coordination (IC)network slicing5Gquality of service (QoS)massive multiple input and multiple output (MIMO) |
| spellingShingle | Jiao Wang Jay Weitzen Oguz Bayat Volkan Sevindik Mingzhe Li Performance Model for Video Service in 5G Networks Future Internet interference coordination (IC) network slicing 5G quality of service (QoS) massive multiple input and multiple output (MIMO) |
| title | Performance Model for Video Service in 5G Networks |
| title_full | Performance Model for Video Service in 5G Networks |
| title_fullStr | Performance Model for Video Service in 5G Networks |
| title_full_unstemmed | Performance Model for Video Service in 5G Networks |
| title_short | Performance Model for Video Service in 5G Networks |
| title_sort | performance model for video service in 5g networks |
| topic | interference coordination (IC) network slicing 5G quality of service (QoS) massive multiple input and multiple output (MIMO) |
| url | https://www.mdpi.com/1999-5903/12/6/99 |
| work_keys_str_mv | AT jiaowang performancemodelforvideoservicein5gnetworks AT jayweitzen performancemodelforvideoservicein5gnetworks AT oguzbayat performancemodelforvideoservicein5gnetworks AT volkansevindik performancemodelforvideoservicein5gnetworks AT mingzheli performancemodelforvideoservicein5gnetworks |