Flexible Functional Split and Fronthaul Delay: A Queuing-Based Model

Díez, Luis; Alba, Alberto Martínez; Kellerer, Wolfgang; Agüero, Ramón

doi:10.1109/access.2021.3124374

Cited by 11 publications

(10 citation statements)

References 59 publications

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“…In this section, we validate the proposed model exploiting an event-driven simulator [14] developed from scratch in C++. This tool considers three different events: (1) arrival of a new packet; (2) transmission of a packet; and (3) change of channel status.…”

Section: Resultsmentioning

confidence: 99%

Finite Buffer Queuing Delay Performance in the Low Earth Orbit Land Mobile Satellite Channel

Marcano

Díez

Agüero

et al. 2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

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Low Earth Orbit (LEO) satellite constellations have been identified for new massive access networks, as a complement to traditional cellular ones, due to their native ubiquity. Despite being a feasible alternative, such networks still raise questions on their performance, in particular regarding the delay and queuing management under realistic channels. In this work, we study the queuing delay of a single satellite-to-ground link, considering a Land Mobile Satellite (LMS) channel in LEO with finite buffer lengths. We analyze the trade-off between delay and packet loss probability, using a novel model based on Markov chains, which we assess and extend with an extensive analysis carried out by means of system level simulation. The developed tools capture with accuracy the queuing delay statistical behavior in the S and Ka frequency bands, where LEO communications are planned to be deployed. Our results show that we can use short buffers to ensure less than 5-10% packet loss, with tolerable delays in such bands.

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Section: Resultsmentioning

confidence: 99%

Finite Buffer Queuing Delay Performance in the Low Earth Orbit Land Mobile Satellite Channel

Marcano

Díez

Agüero

et al. 2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

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“…A packet-based network was assumed for the transport of midhaul traffic between the DUs and CUs, however, with a simplified static latency model, without considering the buffering of packets. In [31], the analysis of latencies in a packet-switched fronthaul network is performed using M/M/1 queuing model and Jackson network theory. Since the latency model proposed estimates average end-to-end delays, and not maximum possible delays, it makes it unsuitable for the network design tasks in which strict latency requirements are imposed.…”

Section: Related Workmentioning

confidence: 99%

Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

2023

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Packet-switched xHaul networks based on Ethernet technology are considered a promising solution for assuring convergent, cost-effective transport of diverse radio data traffic flows in dense 5G radio access networks (RANs). A challenging optimization problem in such networks is the placement of distributed processing units (DUs), which realize a subset of virtualized baseband processing functions on general-purpose processors at selected processing pool (PP) facilities. The DU placement involves the problem of routing of related fronthaul and midhaul data flows between network nodes. In this work, we focus on developing optimization methods for joint placement of DUs and routing of flows with the goal to minimize the overall cost of PPs activation and processing in the network, which we refer to as the PPC-DUP-FR problem. We account for limited processing and transmission resources as well as for stringent latency requirements of data flows in 5G RAN. The latency constraint makes the problem particularly difficult in a packet-switched xHaul network since it involves the non-linear and dynamic estimation of the latencies caused by buffering of packets in the switches. The latency model that we apply in this work is based on worst-case calculations with improved latency estimations that skip from processing the co-routed, but non-affecting flows. We use a mixed-integer programming (MIP) approach to formulate and solve the PPC-DUP-FR optimization problem. Moreover, we develop a heuristic method that provides optimized solutions to larger PPC-DUP-FR problem instances, which are too complex for the MIP method. Numerical experiments performed in different network scenarios indicate on the effectiveness of the heuristic in solving the PPC-DUP-FR problem. In particular, the heuristic achieves up to 63% better results than MIP (at the MIP optimality gap equal to 76%) in a medium-size mesh network, in which the MIP problem is unsolvable for higher traffic demands within reasonable runtime limits. In larger networks, MIP is able to provide some results only for the PPC-DUP-FR problem instances with very low traffic demands, whereas the solutions generated by the heuristic are at least 83% better than the ones achieved with MIP. Also, the analysis performed shows a significant impact of the PP cost factors considered and of the level of cost differentiation of PP nodes on the overall PP cost in the network. Finally, simulation results of a case-study packet xHaul network confirm the correctness of the latency model used.

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“…Other studies have examined the deployment of VNFs and allocation of virtual compute resources [42], the analysis of various functional split options under energy and cost constraints [49], the mapping of RAN slices with a special emphasis on isolation and resource allocation [50], the placement of VNFs [51], the scheduling of VNFs for RAN slices [52], the impact of functional split on fronthaul delay in a RAN slice [53], and the sharing of VNFs across multiple network slice instances (NSIs) [54]. For the sake of clarity, the aforementioned theoretical models are compared with respect to a number of attributes in Tab.…”

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

confidence: 99%

“…The 3GPP initially considered option #2 as a functional split between the vCU and vDU and option #7 or option #8 between the vDU and RU [53]. However, other SDOs, such as Small Cell Forum (SCF), evolved common public radio interface (eCPRI) Cooperation Group, and O-RAN Alliance moved further in identifying new options for functional-splits in the NG-RAN architecture while taking user density, bandwidth, and latency requirements of RAN slices into account.…”

Section: B the Network Function Layermentioning

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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Flexible Functional Split and Fronthaul Delay: A Queuing-Based Model

Cited by 11 publications

References 59 publications

Finite Buffer Queuing Delay Performance in the Low Earth Orbit Land Mobile Satellite Channel

Finite Buffer Queuing Delay Performance in the Low Earth Orbit Land Mobile Satellite Channel

Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

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

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