A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization

Mobile Edge Computing (MEC) technology brings the unprecedented computing capacity to the edge of mobile network. It provides the cloud and end user swift high-quality services with seamless integration of mobile network and Internet. With powerful capability, virtualized network functions can be allocated to MEC. In this paper, we study QoS guaranteed multicasting routing with Network Function Virtualization (NFV) in MEC. Specifically, data should pass through a service function chain before reaching destinations along a multicast tree with minimal computational cost and meeting QoS requirements. Furthermore, to overcome the problems of traditional IP multicast and software-defined multicasting approaches, we propose an implementable multicast mechanism that delivers data along multicast tree but uses unicast sessions. We finally evaluate the performance of the proposed mechanism based on experimental simulations. The results show that our mechanism outperforms others reported in the literature.

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Multi-Source Reliable Multicast Routing with QoS Constraints of NFV in Edge Computing

Electronics ◽

10.3390/electronics8101106 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1106 ◽

Cited By ~ 4

Author(s):

Shiming He He ◽

Kun Xie ◽

Xuhui Zhou ◽

Thabo Semong ◽

Jin Wang

Keyword(s):

Heuristic Algorithms ◽

Multicast Routing ◽

Multicast Tree ◽

Edge Computing ◽

Network Function Virtualization ◽

Reliable Multicast ◽

Network Function ◽

Tree Construction ◽

Service Function Chain ◽

The Cost

Edge Computing (EC) allows processing to take place near the user, hence ensuring scalability and low latency. Network Function Virtualization (NFV) provides the significant convenience of network layout and reduces the service operation cost in EC and data center. Nowadays, the interests of the NFV layout focus on one-to-one communication, which is costly when applied to multicast or group services directly. Furthermore, many artificial intelligence applications and services of cloud and EC are generally communicated through groups and have special Quality of Service (QoS) and reliable requirements. Therefore, we are devoted to the problem of reliable Virtual Network Function (VNF) layout with various deployment costs in multi-source multicast. To guarantee QoS, we take into account the bandwidth, latency, and reliability constraints. Additionally, a heuristic algorithm, named Multi-Source Reliable Multicast Tree Construction (RMTC), is proposed. The algorithm aims to find a common link to place the Service Function Chain (SFC) in the multilevel overlay directed (MOD) network of the original network, so that the deployed SFC can be shared by all users, thereby improving the resource utilization. We then constructed a Steiner tree to find the reliable multicast tree. Two real topologies are used to evaluate the performance of the proposed algorithm. Simulation results indicate that, compared to other heuristic algorithms, our scheme effectively reduces the cost of reliable services and satisfies the QoS requirements.

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Integrating the device-to-device communication technology into edge computing: A case study

Peer-to-Peer Networking and Applications ◽

10.1007/s12083-020-01015-z ◽

2020 ◽

Author(s):

Peiyan Yuan ◽

Rong Huang

Keyword(s):

Communication Technology ◽

Edge Computing ◽

Device To Device ◽

Device To Device Communication

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MD2DO: A Channel Selection Method for Device to Device Communication Using the Mobile Edge Computing (MEC) Paradigm

Mobile Networks and Applications ◽

10.1007/s11036-020-01705-1 ◽

2021 ◽

Author(s):

Rung-Shiang Cheng ◽

Chung-Ming Huang ◽

Hsing-Han Chen

Keyword(s):

Channel Selection ◽

Selection Method ◽

Edge Computing ◽

Mobile Edge Computing ◽

Device To Device ◽

Device To Device Communication

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Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture

Sustainability ◽

10.3390/su12145706 ◽

2020 ◽

Vol 12 (14) ◽

pp. 5706 ◽

Cited By ~ 2

Author(s):

Ricardo S. Alonso ◽

Inés Sittón-Candanedo ◽

Roberto Casado-Vara ◽

Javier Prieto ◽

Juan M. Corchado

Keyword(s):

Reinforcement Learning ◽

Service Providers ◽

Response Times ◽

Cloud Service ◽

Edge Computing ◽

Network Function Virtualization ◽

Software Defined Networks ◽

Learning Mechanisms ◽

Network Function ◽

Learning Techniques

The Internet of Things (IoT) paradigm allows the interconnection of millions of sensor devices gathering information and forwarding to the Cloud, where data is stored and processed to infer knowledge and perform analysis and predictions. Cloud service providers charge users based on the computing and storage resources used in the Cloud. In this regard, Edge Computing can be used to reduce these costs. In Edge Computing scenarios, data is pre-processed and filtered in network edge before being sent to the Cloud, resulting in shorter response times and providing a certain service level even if the link between IoT devices and Cloud is interrupted. Moreover, there is a growing trend to share physical network resources and costs through Network Function Virtualization (NFV) architectures. In this sense, and related to NFV, Software-Defined Networks (SDNs) are used to reconfigure the network dynamically according to the necessities during time. For this purpose, Machine Learning mechanisms, such as Deep Reinforcement Learning techniques, can be employed to manage virtual data flows in networks. In this work, we propose the evolution of an existing Edge-IoT architecture to a new improved version in which SDN/NFV are used over the Edge-IoT capabilities. The proposed new architecture contemplates the use of Deep Reinforcement Learning techniques for the implementation of the SDN controller.

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Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case

Computers ◽

10.3390/computers7040055 ◽

2018 ◽

Vol 7 (4) ◽

pp. 55 ◽

Cited By ~ 9

Author(s):

Giorgio Cattaneo ◽

Fabio Giust ◽

Claudio Meani ◽

Daniele Munaretto ◽

Pietro Paglierani

Keyword(s):

High Performance ◽

End Users ◽

Network Function Virtualization ◽

Radio Link ◽

Management Level ◽

Network Function ◽

High Data ◽

Data Rates ◽

Multi Access ◽

Over The Top

Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high data rates locally achieved, and real-time information about radio link status or consumer geographical position exploited to develop high-value services. To consolidate network elements and edge applications on the same virtualization infrastructure, network operators aim to combine MEC with Network Function Virtualization (NFV). However, MEC in NFV integration is not fully established yet: in fact, various architectural issues are currently open, even at standardization level. This paper describes a novel MEC in an NFV system which successfully combines, at management level, MEC functional blocks with an NFV Orchestrator, and can neutrally support any “over the top” Mobile Edge application with minimal integration effort. A specific ME app combined with an end-user app for the provision of immersive video services is presented. To provide low latency, CPU-intensive services to end users, the proposed architecture exploits High-Performance Computing resources embedded in the edge infrastructure. Experimental results showing the effectiveness of the proposed architecture are reported and discussed.

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CloudSimSDN‐NFV: Modeling and simulation of network function virtualization and service function chaining in edge computing environments

Software Practice and Experience ◽

10.1002/spe.2755 ◽

2019 ◽

Vol 49 (12) ◽

pp. 1748-1764

Author(s):

Jungmin Son ◽

TianZhang He ◽

Rajkumar Buyya

Keyword(s):

Modeling And Simulation ◽

Edge Computing ◽

Network Function Virtualization ◽

Service Function ◽

Network Function ◽

Computing Environments

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Detecting and mitigating cyberattacks using software defined networks for integrated clinical environments

Peer-to-Peer Networking and Applications ◽

10.1007/s12083-021-01082-w ◽

2021 ◽

Author(s):

Alberto Huertas Celdrán ◽

Kallol Krishna Karmakar ◽

Félix Gómez Mármol ◽

Vijay Varadharajan

Keyword(s):

Mobile Networks ◽

Edge Computing ◽

Network Function Virtualization ◽

Mobile Edge Computing ◽

Network Function ◽

Computing Paradigm ◽

Management Processes ◽

Clinical Scenarios ◽

The Future ◽

Clinical Environments

AbstractThe evolution of integrated clinical environments (ICE) and the future generations of mobile networks brings to reality the hospitals of the future and their innovative clinical scenarios. The mobile edge computing paradigm together with network function virtualization techniques and the software-defined networking paradigm enable self-management, adaptability, and security of medical devices and data management processes making up clinical environments. However, the logical centralized approach of the SDN control plane and its protocols introduce new vulnerabilities which affect the security of the network infrastructure and the patients’ safety. The paper at hand proposes an SDN/NFV-based architecture for the mobile edge computing infrastructure to detect and mitigate cybersecurity attacks exploiting SDN vulnerabilities of ICE in real time and on-demand. A motivating example and experiments presented in this paper demonstrate the feasibility of of the proposed architecture in a realistic clinical scenario.

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