SDN/NFV-based end to end network slicing for 5G multi-tenant networks

Chartsias, Panteleimon Konstantinos; Amiras, A.; Plevrakis, I.; Σαμαράς, Ιωακείμ Κ.; Κατσαρός, Κωνσταντίνος; Kritharidis, Dimitrios; Trouva, Eleni; Angelopoulos, Y.; Kourtis, A.; Siddiqui, Muhammad Shuaib; Viñes, A.; Escalona, Eduard

doi:10.1109/eucnc.2017.7980670

Cited by 29 publications

(16 citation statements)

References 1 publication

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“…Modern networking technologies move away from traditional solutions, which require specialized networking hardware and are challenging to maintain and configure. It is envisioned that 6G networks will be developed over SDN [5] which separate the control and data planes at the network devices. The notion of separation simplifies hardware requirements for network devices and increases flexibility as we can program them from a centralized controller.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…Even though we talk of networks, these applications require services from both networking and computing. Modern networking technologies such as software defined networks (SDN) [5] and and computing technologies like Kubernets (K8) [6] and dockers [7] enable virtualization of networks and applications, which allows each application to have its own network and computing resources (refer Section II). Thus, service providers must jointly design and adapt their networking and computing resources to satisfy the diverse E2E QoE requirements.…”

Section: Introductionmentioning

confidence: 99%

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Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Roy¹,

Rao²,

Alpcan³

et al. 2022

Preprint

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Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modelling achieves E2E QoE with approximately 99.8% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Roy¹,

Rao²,

Alpcan³

et al. 2022

Preprint

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show abstract

“…At the same time, NFV is a technology that enables flexible and fast deployment of network functions in commodity devices instead of dedicated purpose-built hardware (Zeng et al 2017). The combination of NFV and SDN brings several advantages for the network operator such as energy efficiency, network programmability (Miozzo et al 2018;De Souza et al 2018), network slicing (Ordonez-Lucena et al 2017;Chartsias et al 2017;Zhang et al 2015;ETSI 2013;Zhou et al 2016;Schiller et al 2015), and dynamic bandwidth adjustment to reduce the delay (Zhang et al 2015;Jahan 2015). For example, it is possible to identify the optimal resources to meet a specific demand and allocate them into the network using SDN/NFV (De Souza et al 2018).…”

Section: Network Function Virtualisationmentioning

confidence: 99%

Flying to the Clouds: The Evolution of the 5G Radio Access Networks

Gonçalves

Santos

Ferreira

et al. 2020

The Cloud-to-Thing Continuum

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The number of connected devices and the amount of data traffic exchanged through mobile networks is expected to double in the near future. Long Term Evolution (LTE) and fifth generation (5G) technologies are evolving to support the increased volume, variety and velocity of data and new interfaces the Internet of Things demands. 5G goes beyond increasing data throughput, providing broader coverage and reliable

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“…In particular, there is a great interest in SDN-based backhauling due to the dominant role that these networks will have in future 5G systems, especially in presence of UDNs and new paradigms such as Cloud-RAN, caching, fog/edge computing [10][11][12]. In this area, recent papers focus on specific services provided by an SDN-based backhaul network, such as mobility management [13][14][15], multi-tenant network slicing [16,17], caching [18,19] and cross-layer coordination [20].…”

Section: Related Literaturementioning

confidence: 99%

SDN-Based Routing for Backhauling in Ultra-Dense Networks

Marabissi

Fantacci

Simoncini

2019

JSAN

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Ultra-Dense Network (UDN) deployment is considered a key element to achieve the requested capacity in future fifth-generation (5G) mobile networks. Backhaul networks in UDNs are formed by heterogeneous links with multi-hop connections and must handle massive traffic. Backhauling in future 5G networks may represent the capacity bottleneck. Therefore, there is the need for efficient and flexible routing schemes able to handle the dynamism of the traffic load in capacity-limited networks. Toward this goal, the emerging Software-Defined Network (SDN) paradigm provides an efficient solution, transferring the routing operation from the data plane switches to a central controller, thus achieving more flexibility, efficiency, and faster convergence time in comparison to conventional networks. This paper proposes and investigates an SDN-approach for an efficient routing in a capacity-limited backhaul network that carries data and control traffic of a heterogeneous UDN. The routing algorithm is centralized in the SDN controller and two different types of traffic flow are considered: data and control plane coordination traffic. The goal is to reduce or even to avoid the amount of traffic that the backhaul network is not able to support, distributing in a fair way the eventual lack of bandwidth among different access points. Simulation results show that with the considered approach the performance significantly improves, especially when there is an excess of traffic load in the network. Moreover, thanks to the SDN-based design, the network can reconfigure the traffic routing depending on the changing conditions.

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SDN/NFV-based end to end network slicing for 5G multi-tenant networks

Cited by 29 publications

References 1 publication

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Achieving AI-enabled Robust End-to-End Quality of Experience over Radio Access Networks

Flying to the Clouds: The Evolution of the 5G Radio Access Networks

SDN-Based Routing for Backhauling in Ultra-Dense Networks

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