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

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.

scholarly journals QoS-Based Multicast Routing in Network Function Virtualization-Enabled Software-Defined Mobile Edge Computing Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shimin Sun ◽  
Xinchao Zhang ◽  
Wentian Huang ◽  
Aixin Xu ◽  
Xiaofan Wang ◽  
...  
Keyword(s):  
Multicast Routing ◽  
Computational Cost ◽  
Mobile Network ◽  
Multicast Tree ◽  
Edge Computing ◽  
Network Function Virtualization ◽  
Mobile Edge Computing ◽  
Seamless Integration ◽  
Network Function ◽  
Pass Through

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.

scholarly journals Orchestration Architecture for Automatic Deployment of 5G Services from Bare Metal in Mobile Edge Computing Infrastructure

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Enrique Chirivella-Perez ◽  
Jose M. Alcaraz Calero ◽  
Qi Wang ◽  
Juan Gutiérrez-Aguado
Keyword(s):  
Mobile Networks ◽  
Edge Computing ◽  
Superior Performance ◽  
Bare Metal ◽  
Mobile Edge Computing ◽  
Computing Paradigm ◽  
Fifth Generation ◽  
5G Network ◽  
Service Deployment ◽  
Deployment Time

The progress in realizing the Fifth Generation (5G) mobile networks has been accelerated recently towards deploying 5G prototypes with increasing scale. One of the Key Performance Indicators (KPIs) in 5G deployments is the service deployment time, which should be substantially reduced from the current 90 hours to the target 90 minutes on average as defined by the 5G Public-Private Partnership (5G-PPP). To achieve this challenging KPI, highly automated and coordinated operations are required for the 5G network management. This paper addresses this challenge by designing and prototyping a novel 5G service deployment orchestration architecture that is capable of automating and coordinating a series of complicated operations across physical infrastructure, virtual infrastructure, and service layers over a distributed mobile edge computing paradigm, in an integrated manner. Empirical results demonstrate the superior performance achieved, which meets the 5G-PPP KPI even in the most challenging scenario where 5G services are installed from bare metal.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Artificial Intelligence Techniques for Information Security in 5G IoT Environments

2020 ◽  
Vol 5 (11) ◽  
pp. 1328-1333
Author(s):  
Ivan Petrov ◽  
Toni Janevski
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
Cost Benefit Analysis ◽  
Network Virtualization ◽  
Cost Benefit ◽  
Telecommunication Networks ◽  
Operation Management ◽  
Network Function Virtualization ◽  
Network Function ◽  
The Future

The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.

Virtualization Evolution

Web Services ◽  
2019 ◽  
pp. 1762-1789
Author(s):  
Harilaos Koumaras ◽  
Christos Damaskos ◽  
George Diakoumakos ◽  
Michail-Alexandros Kourtis ◽  
George Xilouris ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Defined Networking ◽  
Telecommunications Industry ◽  
Network Function Virtualization ◽  
Future Perspectives ◽  
Network Function ◽  
Computing Paradigm ◽  
Cloud Networking

This chapter discusses the evolution of the cloud computing paradigm and its applicability in various sections of the computing and networking/telecommunications industry, such as the cloud networking, the cloud offloading, and the network function virtualization. The new heterogeneous virtualized ecosystem that is formulated creates new needs and challenges for management and administration at the network part. For this purpose, the approach of Software-Defined Networking is discussed and its future perspectives are further analyzed.

Exploring SDN & NFV in 5G Using ONOS & POX Controllers

2018 ◽  
Vol 10 (4) ◽  
pp. 46-60 ◽  
Author(s):  
Christos Bouras ◽  
Anastasia Kollia ◽  
Andreas Papazois
Keyword(s):  
Operating System ◽  
Mobile Networks ◽  
Data Center ◽  
Central Office ◽  
Software Defined Networking ◽  
Theoretical Research ◽  
Network Function Virtualization ◽  
Network Function ◽  
Cloud Networking ◽  
Experimental Findings

This article describes how novel functionalities will take advantage of the cloud networking and will gradually replace the existing infrastructure of mobile networks with a virtualized one. Two technologies, namely software defined networking (SDN) and network function virtualization (NFV), offer their important benefits and a combination of them is an answer to the demands raised, such as central office re-architected as a data center (CORD). Open network operating system (ONOS) and POX are SDN controllers and offer an option to combine SDN and NFV addressing many ongoing problems in the field of mobile networks. In this paper, technologies and both controllers are compared and contrasted. Indicative cases of topologies are simulated and help evaluating both controllers. According to the experimental findings, ONOS is one of the most important controllers for practical, theoretical, research and educational purposes, while POX is a useful and simpler controller for other educative applications.

scholarly journals VNF Placement Optimization at the Edge and Cloud †

2019 ◽  
Vol 11 (3) ◽  
pp. 69 ◽  
Author(s):  
Aris Leivadeas ◽  
George Kesidis ◽  
Mohamed Ibnkahla ◽  
Ioannis Lambadaris
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Research Area ◽  
End Users ◽  
Network Function Virtualization ◽  
Future Research ◽  
Cloud Infrastructure ◽  
Network Function ◽  
Computing Paradigm ◽  
Network Functions

Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers’ goal of low deployment costs.

scholarly journals Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 259 ◽  
Author(s):  
Jie Yuan ◽  
Erxia Li ◽  
Chaoqun Kang ◽  
Fangyuan Chang ◽  
Xiaoyong Li
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Network Architecture ◽  
Social Trust ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Internet Technologies ◽  
Architecture Model ◽  
Security Issues ◽  
Cooperative Relationship

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

scholarly journals NFVMon: Enabling Multioperator Flow Monitoring in 5G Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Enrique Chirivella-Perez ◽  
Juan Gutiérrez-Aguado ◽  
Jose M. Alcaraz-Calero ◽  
Qi Wang
Keyword(s):  
Mobile Networks ◽  
Communication Systems ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
High Definition ◽  
Flow Monitoring ◽  
Fifth Generation ◽  
New Generation ◽  
Monitoring Architecture

With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management plane of the Cloud Computing. NFVMon has been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid-sized infrastructure testbed.

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