Joint Network Slicing and Mobile Edge Computing in 5G Networks

Xiang, Bin; Elias, J. E.; Martignon, Fabio; Nitto, Elisabetta Di

doi:10.1109/icc.2019.8762029

Cited by 37 publications

(28 citation statements)

References 36 publications

(42 reference statements)

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“…Important efforts [103], [104]- [108] have recently been devoted to the introduction of network slicing in MEC. Authors of [104] proposed in a MEC orchestration/management architecture enabled 5G network slicing.…”

Section: Network Slicing In Mec 1) Network Slicing Definitionmentioning

confidence: 99%

“…In [43] a VNF placement approach is proposed which considers the existence of edge servers and cloud servers in order to provide network services Optimization approach SDN NFV SFC 5G Slicing Yala et al [38] • • Cziva et al [40] • • Yang et al [41] • • Leivadeas et al [43] • Kaur et al [61] • [114] • • Ren et al [115] • Yang et al [116] • • Son et al [44] • • • Alameddine et al [117] • • Chen et al [118] • Li et al [119] • • • Chen et al [120] • Ahn et al [121] • • Li et al [122] • • Liang et al [123] • Blanco et al [126] • • Yang et al [124] • Liu et al [125] • Zhang et al [127] • Chen et al [128] • • Guo et al [131] • Tran et al [132] • Zhang et al [133] • Zhao et al [135] • Wang et al [143] • • • Sanchez et al [111] • Tun et al [145] • Martiradonna et al [146] • Taleb et al [113] • • • Mena et al [103] • Wang t al. [112] • Xiang et al [147] • • Tun et al [144] •…”

Section: Optimization Approaches For Combined Resourcesmentioning

confidence: 99%

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Multi-Access Edge Computing: A Survey

et al. 2020

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Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

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Section: Network Slicing In Mec 1) Network Slicing Definitionmentioning

confidence: 99%

Section: Optimization Approaches For Combined Resourcesmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

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“…Moving on from the existing survey on the combination of MEC and network slicing, the purpose of this paper is to address the resource allocation problem of end-to-end network slicing in response to the dynamic traffic and strict service requirements of network slices. Several studies have been paying attention to resource provisioning optimized for service using network slicing in an MEC environment [10], [11]. In [10], a hierarchical radio resource allocation architecture is proposed to reduce the burden of centralized resource allocation burden.…”

Section: Related Workmentioning

confidence: 99%

“…The resource allocation problem considered in the paper only focuses on radio resources, and it is infeasible to keep the changing resource distribution because of the huge operational expenditure. In [11], Xiang et al proposed an optimization algorithm to perform a joint slicing of wireless network and edge computation resources, minimizing the total latency of transmitting, outsourcing, and processing user traffic into account. If the resource allocation problem is formulated to minimize the delay, the slices may greedily consume the resources of low-layer servers without appropriately placing VNFs to other layers of the MEC network.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

Kim

Lim

2021

IEEE Access

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To meet the explosive growth of mobile traffic, the 5G network is designed to be flexible and support multi-access edge computing (MEC), thereby improving the end-to-end quality of service (QoS). In particular, 5G network slicing, which allows a physical infrastructure to split into multiple logical networks, keeps the balance of network resource allocation among different service types with on-demand resource requests. However, achieving effective resource allocation across the end-to-end network is difficult due to the dynamic characteristics of slicing requests such as uncertain real-time resource demand and heterogeneous requirements. In this paper, we develop a reinforcement learning (RL)-based dynamic resource allocation framework for end-to-end network slicing with heterogeneous requirements in multi-layer MEC environments. We first design a hierarchical MEC architecture and formulate a resource allocation problem for the end-to-end network slicing as an optimization problem using the Markov decision process (MDP). Using proximal policy optimization (PPO), we develop independently-collaborative and jointlycollaborative dynamic resource allocation algorithms to maximize resource efficiency while satisfying the QoS of slices. Experimental results show that the proposed algorithms can recognize the characteristics of slice requests and coming resource demands and efficiently allocate resources with a high QoS satisfaction rate.INDEX TERMS 5G, network slicing, multi-access edge computing, network resource management, multiagent reinforcement learning.

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“…For example, the authors in [28]- [31] address the trade-off between the power consumption and the task processing delay. The authors in [32], propose an optimization model to perform joint slicing of mobile network and edge computation resources. As VNF and MEC are two strongly connected technologies, the research evolution of the one affects the evolution of the other.…”

Section: A Related Workmentioning

confidence: 99%

An End-to-End Performance Analysis for Service Chaining in a Virtualized Network

Fountoulakis

Liao

Παππάς

2020

IEEE Open J. Commun. Soc.

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Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). Besides latency constraints, these services may have strict function chaining requirements. In other words, each service has to be processed by a set of network functions in a specific order. The distribution of network functions over different hosts and more flexible routing caused by service function chaining raise new challenges for end-to-end performance analysis. In this paper, as a first step, we analyze an end-to-end communications system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. To approximate the behavior of the system, we decompose the system into subsystems and analyze them independently. By doing so we are able to provide approximate analytical expressions of the performance metrics such as system drop rate, endto-end delay, and system throughput. Then, we show how to apply the similar method to an extended larger system and derive a stochastic model for systems with arbitrary number of servers at the edge.Simulation results show that our approximation model is accurate for the considered systems. We see in Section VI that the simulation and analytical results coincide. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.Recently, the study of the performance of networks in Virtual Network Function (VNF)/Software Defined Network (SDN) environment has attracted a lot of attention, [7]-[14]. Ye et al. [7]analyze the end-to-end delay for embedded VNF chains. They consider two types of services that traverse different VNF chains and provide the delay analysis for each different chain. 3 Miao et al. [9] provide an analytical model based on Stochastic Network Caclulus (SNC) to provide upper and lower delay bounds of a VNF chain. In their analysis, they consider both the case of bursty and non-bursty traffic. Along similar lines, Duan [8] analyzes an end-toend delay performance of service function chaining for particular services and given resources. Authors in [10]-[14], apply tools from queueing theory to evaluate the performance of systems in SDN environment. In particular, Jarschel et al. [12] study the OpenFlow architecture, where the switch is modeled as an M/M/1 queue and the controller as a feedback system of the delay-loss type M/M/1/S queue. Similarly Goto et al. [13] analyze a simple OpenFlow-based switch in SDN environment, however, they distinguish traffic from the controller and exogenous traffic. Furthermore, a reasonable amount of works consider the modeling of connected VNF as a sequence of queues where the goal is to ...

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Joint Network Slicing and Mobile Edge Computing in 5G Networks

Cited by 37 publications

References 36 publications

Multi-Access Edge Computing: A Survey

Multi-Access Edge Computing: A Survey

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

An End-to-End Performance Analysis for Service Chaining in a Virtualized Network

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