Modeling industry 4.0 based fog computing environments for application analysis and deployment

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

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The Role of Edge/Fog Computing Security in IoT and Industry 4.0 Infrastructures

Advances in Library and Information Science - Handbook of Research on Information and Records Management in the Fourth Industrial Revolution ◽

10.4018/978-1-7998-7740-0.ch014 ◽

2021 ◽

pp. 211-222

Author(s):

Meltem Mutluturk ◽

Burcu Kor ◽

Bilgin Metin

Keyword(s):

Cloud Computing ◽

Information And Communication Technologies ◽

Industry 4.0 ◽

Fog Computing ◽

Communication Technologies ◽

The Internet ◽

Current State ◽

Information And Communication ◽

The Internet Of Things

The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.

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Efficiency enhancement method for distributed CAD systems in fog-computing environments

Journal of Physics Conference Series ◽

10.1088/1742-6596/1333/8/082006 ◽

2019 ◽

Vol 1333 ◽

pp. 082006

Author(s):

V M Kureychik ◽

I B Safronenkova

Keyword(s):

Fog Computing ◽

Efficiency Enhancement ◽

Cad Systems ◽

Enhancement Method ◽

Computing Environments

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An Intelligent and Cost-Efficient Resource Consolidation Algorithm in Nanoscale Computing Environments

Applied Sciences ◽

10.3390/app10186494 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6494

Author(s):

MeSuk Kim ◽

ALam Han ◽

TaeYoung Kim ◽

JongBeom Lim

Keyword(s):

Fog Computing ◽

Turnaround Time ◽

Nanoscale Devices ◽

Efficient Resource ◽

Computing Environments ◽

Cost Efficient ◽

Cloud Users ◽

Resource Consolidation ◽

The Internet Of Things ◽

Cloud User

Because the Internet of things (IoT) and fog computing are prevalent, an efficient resource consolidation scheme in nanoscale computing environments is urgently needed. In nanoscale environments, a great many small devices collaborate to achieve a predefined goal. The representative case would be the edge cloud, where small computing servers are deployed close to the cloud users to enhance the responsiveness and reduce turnaround time. In this paper, we propose an intelligent and cost-efficient resource consolidation algorithm in nanoscale computing environments. The proposed algorithm is designed to predict nanoscale devices’ scheduling decisions and perform the resource consolidation that reconfigures cloud resources dynamically when needed without interrupting and disconnecting the cloud user. Because of the large number of nanoscale devices in the system, we developed an efficient resource consolidation algorithm in terms of complexity and employed the hidden Markov model to predict the devices’ scheduling decision. The performance evaluation shows that our resource consolidation algorithm is effective for predicting the devices’ scheduling decisions and efficiency in terms of overhead cost and complexity.

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