EESO: Energy Efficient System-resource Optimization of Multi-Sub-Slice-Connected User in 5G RAN

Ravindran, Sharvari; Chaudhuri, Saptarshi; Bapat, Jyotsna; Das, Debabrata

doi:10.1109/conecct50063.2020.9198455

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…In parallel to the SDOs and industry, a considerable amount of effort has been made in academia to study the extension of virtualization to the RAN architecture and the slicing of underlying resources. The overwhelming majority of available theoretical research focuses on (i) the slicing of radio resources [32]- [39], (ii) the allocation of virtual resources [40]- [42], (iii) the energy consumption of various types of RAN slices [43]- [45], and (iv) the application of AI techniques (notably ML-assisted algorithms) in the operations of RAN architecture [46], [47].…”

Section: ) Conceptual and Analytical Models For Ran Slicing In The Ng...mentioning

confidence: 99%

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Habibi

Yousaf²,

Schotten

2022

IEEE Open J. Commun. Soc.

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The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: (i) to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; (ii) to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization-management and orchestration (NFV-MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and (iii) to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

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Section: ) Conceptual and Analytical Models For Ran Slicing In The Ng...mentioning

confidence: 99%

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Habibi

Yousaf²,

Schotten

2022

IEEE Open J. Commun. Soc.

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“…Following the description of the Katana slicing framework [9], a set of experiments were carried out to evaluate the impact of slicing technologies [10] regarding the energy domain. Katana was previously evaluated in terms of performance in [11,12] and achieved end-to-end latency down to ~10 ms. Related studies [13][14][15][16] have defined the minimal requirement for end-to-end URLLC slicing to be at 20 ms [17,18]; therefore, the presented work significantly improves expected latency performance. For the needs of these experiments, a 5G system configured in Standalone Mode (SA) was used, with a set of 5 UEs.…”

Section: G Slicing Energy Evaluationmentioning

confidence: 99%

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

et al. 2021

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The definition of multiple slicing types in 5G has created a wide field for service innovation in communications. However, the advantages that network slicing has to offer remain to be fully exploited by today’s applications and users. An important area that can potentially benefit from 5G slicing is emergency communications for First Responders. The latter consists of heterogeneous teams, imposing different requirements on the connectivity network. In this paper, the RESPOND-A platform is presented, which provides First Responders with network-enabled tools on top of 5G on-scene planning, with enhanced service slicing capabilities tailored to emergency communications. Furthermore, a mapping of emergency services and communications to specific slice types is proposed to identify the current challenges in the field. Additionally, the proposed tentative mechanism is evaluated in terms of energy efficiency. Finally, the approach is summarized by discussing future steps in the convergence of 5G network slicing in various areas of emergency vertical applications.

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“…Lastly, a series of studies [ 7 , 8 , 9 ] attempted to address subslicing and its impact on performance. In [ 7 ], subslices were created for each vertical to include groups of UEs based on their similar SLA values.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 7 ], subslices were created for each vertical to include groups of UEs based on their similar SLA values. In [ 8 ], the focus was on optimizing the resource allocation for services, where UEs can connect to multiple subslices within a slice. The system load was defined as the range of the number of packets and packet sizes that will not fully utilize the allocated bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

Kulmar

Müürsepp

Alam

2023

Sensors

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In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.

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EESO: Energy Efficient System-resource Optimization of Multi-Sub-Slice-Connected User in 5G RAN

Cited by 6 publications

References 10 publications

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

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