A Collaborative Service Deployment and Application Assignment Method for Regional Edge Computing Enabled IoT

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

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Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

Security and Communication Networks ◽

10.1155/2020/8822990 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jianbing Zhang ◽

Bowen Ma ◽

Jiwei Huang

Keyword(s):

Optimization Problem ◽

Real Life ◽

Computer Software ◽

Edge Computing ◽

Simulation Experiments ◽

Computing Architecture ◽

Mobile Gis ◽

Service Deployment ◽

Quantitative Analyses ◽

Critical Requirement

Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. With the growing popularity and wide application of GIS in reality, performance has become a critical requirement, especially for mobile GIS services. To attack this challenge, this paper tries to optimize the performance of GIS services by deploying them into edge computing architecture which is an emerging computational model that enables efficient offloading of service requests to edge servers for reducing the communication latency between end-users and GIS servers deployed in the cloud. Stochastic models for describing the dynamics of GIS services with edge computing architecture are presented, and their corresponding quantitative analyses of performance attributes are provided. Furthermore, an optimization problem is formulated for service deployment in such architecture, and a heuristic approach to obtain the near-optimal performance is designed. Simulation experiments based on real-life GIS performance data are conducted to validate the effectiveness of the approach presented in this paper.

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Resource-aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

IEEE Access ◽

10.1109/access.2021.3102867 ◽

2021 ◽

pp. 1-1

Author(s):

Johirul Islam ◽

Tanesh Kumar ◽

Ivana Kovacevic ◽

Erkki Harjula

Keyword(s):

Edge Computing ◽

Use Case ◽

Healthcare Use ◽

Service Deployment ◽

Resource Aware

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A Survey on Secure Deployment of Mobile Services in Edge Computing

Security and Communication Networks ◽

10.1155/2021/8846239 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Mengmeng Cui ◽

Yiming Fei ◽

Yin Liu

Keyword(s):

Autonomous Vehicles ◽

Mobile Network ◽

Edge Computing ◽

Internet Of Vehicles ◽

It Service ◽

Computing Services ◽

Service Deployment ◽

Cloud Computing Services ◽

Complete Survey ◽

Research Studies

Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

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Exploitation of Mobile Edge Computing in 5G Distributed Mission-Critical Push-to-Talk Service Deployment

IEEE Access ◽

10.1109/access.2018.2849200 ◽

2018 ◽

Vol 6 ◽

pp. 37665-37675 ◽

Cited By ~ 10

Author(s):

Ruben Solozabal ◽

Aitor Sanchoyerto ◽

Eneko Atxutegi ◽

Bego Blanco ◽

Jose Oscar Fajardo ◽

...

Keyword(s):

Edge Computing ◽

Mobile Edge Computing ◽

Service Deployment ◽

Mission Critical

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GoDeep: Intelligent IoV Service Deployment and Execution with Privacy Preservation in Cloud-edge Computing

10.1109/icws53863.2021.00079 ◽

2021 ◽

Author(s):

Wentao Liu ◽

Xiaolong Xu ◽

Lianyong Qi ◽

Xuyun Zhang ◽

Wanchun Dou

Keyword(s):

Privacy Preservation ◽

Edge Computing ◽

Service Deployment

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Toward Reinforcement-Learning-Based Service Deployment of 5G Mobile Edge Computing with Request-Aware Scheduling

IEEE Wireless Communications ◽

10.1109/mwc.001.1900298 ◽

2020 ◽

Vol 27 (1) ◽

pp. 84-91 ◽

Cited By ~ 4

Author(s):

Yanlong Zhai ◽

Tianhong Bao ◽

Liehuang Zhu ◽

Meng Shen ◽

Xiaojiang Du ◽

...

Keyword(s):

Reinforcement Learning ◽

Edge Computing ◽

Mobile Edge Computing ◽

Service Deployment

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A Novel Genetic Service Function Deployment Management Platform for Edge Computing

Mobile Information Systems ◽

10.1155/2020/8830294 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22

Author(s):

David Chunhu Li ◽

Bo-Hun Chen ◽

Chia-Wei Tseng ◽

Li-Der Chou

Keyword(s):

Service Providers ◽

Edge Computing ◽

The Internet ◽

Low Latency ◽

Network Efficiency ◽

Management Platform ◽

Technology Service ◽

Service Deployment ◽

Network Latency ◽

Network Environments

The various applications of the Internet of Things and the Internet of Vehicles impose high requirements on the network environment, such as bandwidth and delay. To meet low-latency requirements, the concept of mobile edge computing has been introduced. Through virtualisation technology, service providers can rent computation resources from the infrastructure of the network operator, whereas network operators can deploy all kinds of service functions (SFs) to the edge network to reduce network latency. However, how to appropriately deploy SFs to the edge of the network presents a problem. Apart from improving the network efficiency of edge computing service deployment, how to effectively reduce the cost of service deployment is also important to achieve a performance-cost balance. In this paper, we present a novel SF deployment management platform that allows users to dynamically deploy edge computing service applications with the lowest network latency and service deployment costs in edge computing network environments. We describe the platform design and system implementation in detail. The core platform component is an SF deployment simulator that allows users to compare various SF deployment strategies. We also design and implement a genetic algorithm-based service deployment algorithm for edge computing (GSDAE) in network environments. This method can reduce the average network latency for a client who accesses a certain service for multiple tenants that rent computing resources and subsequently reduce the associated SF deployment costs. We evaluate the proposed platform by conducting extensive experiments, and experiment results show that our platform has a practical use for the management and deployment of edge computing applications given its low latency and deployment costs not only in pure edge computing environments but also in mixed edge and cloud computing scenarios.

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