Analysis of evolutionary process of fog computing system based on BA and ER network hybrid model

2019 ◽  
Vol 13 (1) ◽  
pp. 33-38
Author(s):  
Kunpeng Kang
Author(s):  
А.Н. ВОЛКОВ

Одним из направлений развития сетей связи 5G и сетей связи 2030 является интегрирование в сеть распределенных вычислительных структур, таких как системы пограничных и туманных вычислений (Fog), которые призваны выполнить децентрализацию вычислительной части сетей. В связи с этим необходимо исследовать и определить принципы предоставления услуг на основе распределенной вычислительной инфраструктуры, в том числе в условиях ограниченности ресурсов отдельно взятых составных частей (Fog-устройства). Предлагается новый фреймворк распределенной динамической вычислительной системы туманных вычислений на основе микросервисного архитектурного подхода к реализации, развертыванию и миграции программного обеспечения предоставляемых услуг. Исследуется типовая архитектура микросервисного подхода и ее имплементация в туманные вычисления, а также рассматриваются два алгоритма: алгоритм K-средних для нахождения центра пользовательской нагрузки и алгоритм роевой оптимизации для определения устройства тумана с необходимыми характеристиками для последующей миграции микросервиса. One of the directions of 5G and 2030 communications networks development is the network-integrated distributed structures, such as edge computing (MEC) and Fog computing, which are designed to decentralize the computing part of networks. In this regard, it is necessary to investigate and determine the principles of providing services based on a distributed computing infrastructure, including in conditions of limited resources of individual components (Fog devices). This article proposes a new framework for a distributed dynamic computing system of fog computing based on a microservice architectural approach to the implementation, deployment, and software migration of the services. The article examines the typical architecture of the microservice approach and its implementation in fog computing, and also investigates two algorithms: K-means for finding the center of user load, swarm optimization (PSO) to determine the fog device with the necessary characteristics for the subsequent migration of the microservice.


2020 ◽  
Vol 7 (6) ◽  
pp. 4898-4911 ◽  
Author(s):  
Qinqin Tang ◽  
Renchao Xie ◽  
Fei Richard Yu ◽  
Tao Huang ◽  
Yunjie Liu

Author(s):  
Arumugam So Raman

This paper is documenting the potential of Fog Computing in Education. First, this study explores the difference between cloud computing and Fog Computing. Then the features of computing explained briefly. A tremendous increase in Internet usage among the people does not allow the sustainability to continue depending on Cloud Computing as a centralized web server, due to the truth that Cloud Computing system allows access to internet data as well as therefore making it feasible for users to availability, share along with store information in remote servers. With Fog Computing, multiple users, gadgets such as automobiles, wearable gizmos, sensing units, wise gadgets, an organization can accept one another utilizing their very own Fog facilities. In the educational sector, Fog computing technology boosts educational operations and provides a platform with agility, versus slowing them down or quitting them. Fog computing is a modern technology that is set for high development in the future, as well as will substantially improve day-to-day procedures for many sectors, including education. Finally, security issues and challenges of implementation Fog computing discussed.


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