Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165377
C. Manso, R. Vilalta, R. Casellas, R. Martínez, R. Muñoz
Current SDN controllers are monolithic applications that are run on dedicated servers and require specific protocols for synchronization among them. These SDN controllers do not provide the required flexibility to scale-out in case of a cloud-scale number of connectivity service requests. In this demonstration, We present the control of transport networks based on ONF Transport API using a cloud-native SDN controller based on micro-services. This demo provides insights on novel software implementations of transport network control technologies, such as container-based control architectures and standard interfaces. The proposed SDN controller components synchronize among them using gRPC protocol and a defined protocol buffer.
{"title":"Cloud-native SDN Controller Based on Micro-Services for Transport Networks","authors":"C. Manso, R. Vilalta, R. Casellas, R. Martínez, R. Muñoz","doi":"10.1109/NetSoft48620.2020.9165377","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165377","url":null,"abstract":"Current SDN controllers are monolithic applications that are run on dedicated servers and require specific protocols for synchronization among them. These SDN controllers do not provide the required flexibility to scale-out in case of a cloud-scale number of connectivity service requests. In this demonstration, We present the control of transport networks based on ONF Transport API using a cloud-native SDN controller based on micro-services. This demo provides insights on novel software implementations of transport network control technologies, such as container-based control architectures and standard interfaces. The proposed SDN controller components synchronize among them using gRPC protocol and a defined protocol buffer.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114213529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165427
José Jurandir Alves Esteves, Amina Boubendir, F. Guillemin, Pierre Sens
Network Slicing has its roots in Network Function Virtualization (NFV) allowing high flexibility in the delivery of end-to-end network services. To achieve Network Slicing promises on efficiency, Network Slice Providers have to ensure optimized resource utilization and to guarantee Quality of Service when managing the life-cycle of a Network Slice. We focus in this paper on Network Slice Placement, intimately related to the VNF Placement and Chaining problem. In contrary to most studies related to VNF placement, we deal with the most complete and complex Network Slice topologies and we pay special attention to the geographic location of Network Slice Users. We propose a data model adapted to Integer Linear Programming. Extensive numerical experiments assess the relevance of taking into account the user location constraints.
{"title":"Location-based Data Model for Optimized Network Slice Placement","authors":"José Jurandir Alves Esteves, Amina Boubendir, F. Guillemin, Pierre Sens","doi":"10.1109/NetSoft48620.2020.9165427","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165427","url":null,"abstract":"Network Slicing has its roots in Network Function Virtualization (NFV) allowing high flexibility in the delivery of end-to-end network services. To achieve Network Slicing promises on efficiency, Network Slice Providers have to ensure optimized resource utilization and to guarantee Quality of Service when managing the life-cycle of a Network Slice. We focus in this paper on Network Slice Placement, intimately related to the VNF Placement and Chaining problem. In contrary to most studies related to VNF placement, we deal with the most complete and complex Network Slice topologies and we pay special attention to the geographic location of Network Slice Users. We propose a data model adapted to Integer Linear Programming. Extensive numerical experiments assess the relevance of taking into account the user location constraints.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123271713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165449
Takahiro Hirayama, M. Jibiki, Ved P. Kafle
Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. NFV platforms are required to offer stable and guaranteed quality-of-service (QoS)even during dynamically changing resource demands and traffic volumes. To meet the QoS requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach for proactive control. In this paper, we propose a traffic prediction framework based on ensemble learning, comprising weak regressors trained by ML models, such as recurrent neural networks (RNNs), random forest, and elastic net. It was observed that the prediction error tends to worsen with time because the gap of trends between the past and future traffics becomes wider. Therefore, to reduce the prediction errors, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble. The evaluation result with real traffic data verified that the resource adjustment scheme based on the proposed traffic prediction framework keeps the frequency of over and under provisioning low, which is lesser by 45% in comparison to RNNs and autoregressive moving average (ARMA).
网络功能虚拟化(Network function virtualization, NFV)使网络运营商能够灵活地为物联网、移动应用等业务提供多种虚拟化功能。NFV平台需要在资源需求和流量动态变化的情况下提供稳定且有保障的QoS (quality- service，服务质量)。为了满足时变网络环境下的QoS需求，基础设施提供商必须动态调整分配给虚拟网络功能(VNFs)的计算资源(如CPU)的数量。为了提供灵活的资源控制和适应性，通过机器学习技术预测虚拟服务器负载是一种有效的主动控制方法。在本文中，我们提出了一个基于集成学习的流量预测框架，该框架包括由ML模型训练的弱回归量，如循环神经网络(rnn)、随机森林和弹性网络。据观察，随着时间的推移，由于过去和未来交通量之间的趋势差距越来越大，预测误差也越来越大。因此，为了减小预测误差，我们提出了一种基于遗忘和动态集合的回归量调整机制。实际交通数据的评价结果验证了基于所提出的交通预测框架的资源调整方案保持了较低的供应过剩和供应不足的频率，与rnn和自回归移动平均(ARMA)相比减少了45%。
{"title":"Regressor Relearning Architecture Adapting to Traffic Trend Changes in NFV Platforms","authors":"Takahiro Hirayama, M. Jibiki, Ved P. Kafle","doi":"10.1109/NetSoft48620.2020.9165449","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165449","url":null,"abstract":"Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. NFV platforms are required to offer stable and guaranteed quality-of-service (QoS)even during dynamically changing resource demands and traffic volumes. To meet the QoS requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach for proactive control. In this paper, we propose a traffic prediction framework based on ensemble learning, comprising weak regressors trained by ML models, such as recurrent neural networks (RNNs), random forest, and elastic net. It was observed that the prediction error tends to worsen with time because the gap of trends between the past and future traffics becomes wider. Therefore, to reduce the prediction errors, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble. The evaluation result with real traffic data verified that the resource adjustment scheme based on the proposed traffic prediction framework keeps the frequency of over and under provisioning low, which is lesser by 45% in comparison to RNNs and autoregressive moving average (ARMA).","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116679540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165473
J. Baranda, J. Mangues‐Bafalluy, R. Martínez, L. Vettori, Kiril Antevski, C. Bernardos, Xi Li
5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.
{"title":"5G-TRANSFORMER meets Network Service Federation: design, implementation and evaluation","authors":"J. Baranda, J. Mangues‐Bafalluy, R. Martínez, L. Vettori, Kiril Antevski, C. Bernardos, Xi Li","doi":"10.1109/NetSoft48620.2020.9165473","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165473","url":null,"abstract":"5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116529653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165450
A. Erfanian, F. Tashtarian, Reza Farahani, C. Timmerer, H. Hellwagner
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and extra computational complexity. The emerging software-defined networking (SDN) and network function virtualization (NFV) paradigms enable researchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC) -based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF) functions. At the edge of the network, VRPs are responsible for collecting clients' requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requesting VRPs over unicast paths. To mitigate the time complexity of the proposed MILP model, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using the MiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
{"title":"On Optimizing Resource Utilization in AVC-based Real-time Video Streaming","authors":"A. Erfanian, F. Tashtarian, Reza Farahani, C. Timmerer, H. Hellwagner","doi":"10.1109/NetSoft48620.2020.9165450","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165450","url":null,"abstract":"Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and extra computational complexity. The emerging software-defined networking (SDN) and network function virtualization (NFV) paradigms enable researchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC) -based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF) functions. At the edge of the network, VRPs are responsible for collecting clients' requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requesting VRPs over unicast paths. To mitigate the time complexity of the proposed MILP model, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using the MiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116612692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165337
Vasileios Koutsouvelis, S. Shiaeles, B. Ghita, G. Bendiab
Insider threats are one of the most damaging risk factors for the IT systems and infrastructure of a company or an organization; identification of insider threats has prompted the interest of the world academic research community, with several solutions having been proposed to alleviate their potential impact. For the implementation of the experimental stage described in this study, the Convolutional Neural Network (from now on CNN) algorithm was used and implemented via the Google Tensorflow program, which was trained to identify potential threats from images produced by the available dataset. From the examination of the images that were produced and with the help of Machine Learning, the question whether the activity of each user is classified as “malicious” or not for the Information System was answered.
{"title":"Detection of Insider Threats using Artificial Intelligence and Visualisation","authors":"Vasileios Koutsouvelis, S. Shiaeles, B. Ghita, G. Bendiab","doi":"10.1109/NetSoft48620.2020.9165337","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165337","url":null,"abstract":"Insider threats are one of the most damaging risk factors for the IT systems and infrastructure of a company or an organization; identification of insider threats has prompted the interest of the world academic research community, with several solutions having been proposed to alleviate their potential impact. For the implementation of the experimental stage described in this study, the Convolutional Neural Network (from now on CNN) algorithm was used and implemented via the Google Tensorflow program, which was trained to identify potential threats from images produced by the available dataset. From the examination of the images that were produced and with the help of Machine Learning, the question whether the activity of each user is classified as “malicious” or not for the Information System was answered.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165314
D. Camps-Mur, Ferran Cañellas, A. Machwe, Jorge Paracuellos, Konstantinos Choumas, D. Giatsios, T. Korakis, Hadi Razzaghi Kouchaksaraei
Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.
{"title":"5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services","authors":"D. Camps-Mur, Ferran Cañellas, A. Machwe, Jorge Paracuellos, Konstantinos Choumas, D. Giatsios, T. Korakis, Hadi Razzaghi Kouchaksaraei","doi":"10.1109/NetSoft48620.2020.9165314","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165314","url":null,"abstract":"Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114830400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165396
Mehrdad Hajizadeh, Nima Afraz, M. Ruffini, T. Bauschert
The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.
{"title":"Collaborative Cyber Attack Defense in SDN Networks using Blockchain Technology","authors":"Mehrdad Hajizadeh, Nima Afraz, M. Ruffini, T. Bauschert","doi":"10.1109/NetSoft48620.2020.9165396","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165396","url":null,"abstract":"The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128625342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/NetSoft48620.2020.9165366
Nabhasmita Sen, Antony A Franklin
5G Mobile network will reap the benefits from key technologies like Software Defined Networking and Network Function Virtualization. Cloud Radio Access Network architecture (Cloud RAN) is proven to be a promising architecture, but fully centralized Cloud RAN imposes a great bandwidth requirement in the fronthaul link. Different functional split options for 5G RAN have been proposed which lead to a trade-off between centralization and bandwidth requirement. Functional split at different granularity such as per cell, per logical network (slice), per user, or per bearer, have been an area of interest. To explore the effect of slice granularity in adaptive splits for slices, we formulate slice centric functional split in 5G RAN as an ILP to maximize centralization of baseband processing. By varying the slice granularity from macro slicing to micro slicing, we observe how slice centric split can impact centralization benefit of the network. We show that with increasing slice granularity slice centric split can render more centralization benefit in some scenarios but a trade off exists between centralization benefit and migration cost in the network which should be carefully considered in real deployment scenario.
{"title":"Impact of Slice Granularity in Centralization Benefit of 5G Radio Access Network","authors":"Nabhasmita Sen, Antony A Franklin","doi":"10.1109/NetSoft48620.2020.9165366","DOIUrl":"https://doi.org/10.1109/NetSoft48620.2020.9165366","url":null,"abstract":"5G Mobile network will reap the benefits from key technologies like Software Defined Networking and Network Function Virtualization. Cloud Radio Access Network architecture (Cloud RAN) is proven to be a promising architecture, but fully centralized Cloud RAN imposes a great bandwidth requirement in the fronthaul link. Different functional split options for 5G RAN have been proposed which lead to a trade-off between centralization and bandwidth requirement. Functional split at different granularity such as per cell, per logical network (slice), per user, or per bearer, have been an area of interest. To explore the effect of slice granularity in adaptive splits for slices, we formulate slice centric functional split in 5G RAN as an ILP to maximize centralization of baseband processing. By varying the slice granularity from macro slicing to micro slicing, we observe how slice centric split can impact centralization benefit of the network. We show that with increasing slice granularity slice centric split can render more centralization benefit in some scenarios but a trade off exists between centralization benefit and migration cost in the network which should be carefully considered in real deployment scenario.","PeriodicalId":239961,"journal":{"name":"2020 6th IEEE Conference on Network Softwarization (NetSoft)","volume":"5 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131746489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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