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

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1106 ◽  
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.

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 Priority-Based Offloading and Caching in Mobile Edge Cloud

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fernaz Narin Nur ◽  
Saiful Islam ◽  
Nazmun Nessa Moon ◽  
Asif Karim ◽  
Sami Azam ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Mobile Network ◽  
Edge Computing ◽  
Joint Optimization ◽  
Mobile Edge Computing ◽  
Stage Of Development ◽  
Research Initiative ◽  
Novel Concept ◽  
Reducing Energy ◽  
Task Offloading

Mobile Edge Computing (MEC) is relatively a novel concept in the parlance of Computational Offloading. MEC signifies the offloading of intensive computational tasks to the cloud which is generally positioned at the edge of a mobile network. Being in an embryonic stage of development, not much research has yet been done in this field despite its potential promises. However, with time the advantages are gaining growing attention and MEC is gradually taking over some of the resource-intensive functionalities of a traditional centralized cloud-based system. Another new idea called Task Caching is emerging rapidly with the offloading policy. This joint optimization idea of Task Offloading and caching is relatively a very new concept. It has been in use for reducing energy consumption and delay time for mobile edge computing. Due to the encouraging offshoots from some of the current research on the joint optimization problem, this research initiative aims to take the progress forward. The work improves upon the “prioritization of the tasks” by adopting a very practical approach discussed forward, and proposes a different way for Task Offloading and caching to the edge of the cloud, thereby bringing a significant enhancement to the QoS of MEC.

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

2020 ◽  
Vol 19 (11) ◽  
pp. 2699-2713 ◽  
Author(s):  
Meitian Huang ◽  
Weifa Liang ◽  
Xiaojun Shen ◽  
Yu Ma ◽  
Haibin Kan
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Function

scholarly journals A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Bego Blanco ◽  
Ianire Taboada ◽  
Jose Oscar Fajardo ◽  
Fidel Liberal
Keyword(s):  
Power Consumption ◽  
Robust Optimization ◽  
Edge Computing ◽  
Virtual Network ◽  
Small Cells ◽  
Mobile Edge Computing ◽  
Energy Aware ◽  
Network Function ◽  
The Impact ◽  
Virtual Network Function

In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

scholarly journals Modified reinforcement learning based- caching system for mobile edge computing

2020 ◽  
pp. 1-16
Author(s):  
Sarra Mehamel ◽  
Samia Bouzefrane ◽  
Soumya Banarjee ◽  
Mehammed Daoui ◽  
Valentina E. Balas
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Nodes ◽  
Caching Strategy ◽  
Edge Network ◽  
The Right ◽  
And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

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