AZSPM: Autonomic Zero-Knowledge Security Provisioning Model for Medical Control Systems in Fog Computing Environments

2017 ◽  
Author(s):  
Junaid Chaudhry ◽  
Kashif Saleem ◽  
Rafiqul Islam ◽  
Ali Selamat ◽  
Mudassar Ahmad ◽  
...  
Keyword(s):  
Control Systems ◽  
Fog Computing ◽  
Zero Knowledge ◽  
Medical Control ◽  
Computing Environments

scholarly journals COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

2021 ◽  
pp. 1-1
Author(s):  
Shreshth Tuli ◽  
Shivananda Poojara ◽  
Satish Narayana Srirama ◽  
Giuliano Casale ◽  
Nick Jennings
Keyword(s):  
Fog Computing ◽  
Gradient Based ◽  
Computing Environments ◽  
Container Orchestration

Latency-Aware Application Module Management for Fog Computing Environments

2019 ◽  
Vol 19 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Redowan Mahmud ◽  
Kotagiri Ramamohanarao ◽  
Rajkumar Buyya
Keyword(s):  
Fog Computing ◽  
Computing Environments ◽  
Application Module

scholarly journals Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

2021 ◽  
Vol 11 (22) ◽  
pp. 10996
Author(s):  
Jongbeom Lim
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Industrial Revolution ◽  
Virtual Machines ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Virtualization Technology ◽  
Computing Environments ◽  
One Machine

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.

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

2019 ◽  
Vol 91 ◽  
pp. 48-60 ◽  
Author(s):  
Nandor Verba ◽  
Kuo-Ming Chao ◽  
Jacek Lewandowski ◽  
Nazaraf Shah ◽  
Anne James ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Fog Computing ◽  
Application Analysis ◽  
Computing Environments

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

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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