P4-NetFPGA-based network slicing solution for 5G MEC architectures

Ricart‐Sanchez, Ruben; Malagón, Pedro; Calero, José M. Alcaraz; Wang, Qi

doi:10.1109/ancs.2019.8901889

Cited by 15 publications

(10 citation statements)

References 3 publications

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“…3) Other INC Applications: There are several studies that leverage in-network computing in other 4G/5G/6G applications. Processing of 5G slice flows and 6G tasks has been considered in respectively [154] and [155]. Ricart et al [154] demonstrate an in-network solution which allows the processing of the flows in 5G network slices.…”

Section: G/5g/6gmentioning

confidence: 99%

“…Processing of 5G slice flows and 6G tasks has been considered in respectively [154] and [155]. Ricart et al [154] demonstrate an in-network solution which allows the processing of the flows in 5G network slices. They define a network slice by 6-tuple: 5G user source and destination IPs; 5G user source and destination ports; differentiated services code point; and GTP Tunnel ID which is related to a radio tunnel established between user equipment and 5G core network.…”

Section: G/5g/6gmentioning

confidence: 99%

“…They have leveraged network elements to perform some control plane procedures, or offloadable mobile packet core network functions like Serving Gateway and User Plane Function. The studies in [154], [155], [156], [157], [158] have leveraged in-network computing for faster processing in other areas of 4G/5G/6G including 5G network slicing, 6G applications, LTE serving IoT application, and monitoring/securing 5G networks.…”

Section: E Summary Comparisons Insights and Lessons Learnedmentioning

confidence: 99%

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A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

Kianpisheh

Taleb

2023

IEEE Commun. Surv. Tutorials

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In comparison with cloud computing, edge computing offers processing at locations closer to end devices and reduces the user experienced latency. The new recent paradigm of innetwork computing employs programmable network elements to compute on the path and prior to traffic reaching the edge or cloud servers. It advances common edge/cloud server based computing through proposing line rate processing capabilities at closer locations to the end devices. This paper discusses use cases, enabler technologies and protocols for in-network computing. According to our study, considering programmable data plane as an enabler technology, potential in-network computing applications are in-network analytics, in-network caching, innetwork security, and in-network coordination. There are also technology specific applications of in-network computing in the scopes of cloud computing, edge computing, 5G/6G, and NFV. In this survey, the state of the art, in the framework of the proposed categorization, is reviewed. Furthermore, comparisons are provided in terms of a set of proposed criteria which assess the methods from the aspects of methodology, main results, as well as application-specific criteria. Finally, we discuss lessons learned and highlight some potential research directions.

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Section: G/5g/6gmentioning

confidence: 99%

Section: G/5g/6gmentioning

confidence: 99%

Section: E Summary Comparisons Insights and Lessons Learnedmentioning

confidence: 99%

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A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

Kianpisheh

Taleb

2023

IEEE Commun. Surv. Tutorials

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“…Ricart-Sanchez et al [27,28] propose a NetFPGA-based network slicing solution implemented in P4 language [29] for 5G MEC architectures. Moreover, an analysis and evaluation of the performance is presented.…”

Section: Hardware-based Programmabilitymentioning

confidence: 99%

XDP-Based SmartNIC Hardware Performance Acceleration for Next-Generation Networks

et al. 2022

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Next-generation networks are expected to combine advanced physical and digital technologies in super-high-speed connected system infrastructures, gaining critical operation competitiveness of improved efficiency, productivity and quality of services. Towards a fully digital and connected world, these platforms will enable infrastructure virtualization and support of edge processing, making emerging sectors, such as Industry 4.0, ready to exploit its full potentials. Nevertheless, the fast growth of data-centric and automated systems may exceed the capabilities of the overall infrastructure beyond the radio access networks, becoming unable to fulfil the demands of vertical sectors and representing a bottleneck. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for network providers to deploy highly scalable and prioritisable in-network optimisation schemes to meet industry expectations in next-generation networks. To this end, this work presents a novel framework that leverages extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) to offload network functions to reduce unnecessary overhead in the backbone infrastructure. The proposed solution is envisioned to be implemented as a Network Application (NetApp) service, which will greatly benefit the compatibility with next-generation networking ecosystem empowered by Artificial Intelligence (AI), advanced automation, multi-domain network slicing, and other related technologies. The achieved results demonstrate key performance improvements in terms of packet processing capacity as high as about 18 million packets per second (Mpps), system throughput up to 6.1 Mpps with 0% of packet loss, and illustrate the flexibility of the framework to adapt to multiple network policy rules dynamically on demand.

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“…Recent research trends are exploring selected hardware offloading solutions. For example, FPGA implementation of offloading the GPRS Tunnelling Protocol (GTP) function in the MEC platforms are proposed in [2], or slicing solution based on programmable switch in [3] and, finally, stateless translations of GTP protocol in Segment Routing version 6 in [4]. Offloading of 5G virtualised Radio Access Network (vRAN) functions to programmable hardware has been also proposed [5].…”

Section: Introductionmentioning

confidence: 99%

User Plane Function Offloading in P4 switches for enhanced 5G Mobile Edge Computing

Paolucci

Scano

Cugini

et al. 2021

2021 17th International Conference on the Design of Reliable Communication Networks (DRCN)

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P4-NetFPGA-based network slicing solution for 5G MEC architectures

Cited by 15 publications

References 3 publications

A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

A Survey on In-Network Computing: Programmable Data Plane and Technology Specific Applications

XDP-Based SmartNIC Hardware Performance Acceleration for Next-Generation Networks

User Plane Function Offloading in P4 switches for enhanced 5G Mobile Edge Computing

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