Wide-area smart grids with new smart units synchronized measurement analysis and control based on cloud computing platform

2015 ◽  
Vol 40 (3) ◽  
pp. 362-378 ◽  
Author(s):  
Jianyang Zhao ◽  
Weihong Ding ◽  
Lingwei Zhan ◽  
Heshuai Shao ◽  
Chengfu Sun ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Smart Grids ◽  
Wide Area ◽  
Computing Platform ◽  
Measurement Analysis ◽  
Cloud Computing Platform ◽  
And Control

scholarly journals Design and Implementation of An Integrated Industrial Cloud-Based Measurement and Control System

2019 ◽  
Vol 8 (3) ◽  
pp. 5767-5772
Keyword(s):  
Cloud Computing ◽  
Communication Networks ◽  
Scale Production ◽  
Production Environment ◽  
Measurement And Control System ◽  
Computing Platform ◽  
Measuring Devices ◽  
Cloud Computing Platform ◽  
And Control ◽  
Measurement And Control

In a industrial-scale production environment, many factory parameters need to be collected in real time are hard to integrate and synchronous operate. The solution is still being researched and developed by major industrial and technology companies in the world. The article aims to propose a method to integrate a system for measuring and control with cloud computing servers via the Internet to monitor and control the factory operating parameters on a cloud computing platform. Specifically, the authors will describe the mechanism of data collection from measuring devices and control via industrial communication networks, Internet and cloud computing server to ensure the system accessibility from anytime and anywhere

Collaborative management and control of blockchain in cloud computing environment

2020 ◽  
pp. 1-11
Author(s):  
Guanghai Tang ◽  
Hui Zeng
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Consensus Algorithm ◽  
Collaborative Management ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
System Load ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
And Control

Cloud computing, as a product of the fusion and development of computer technology and Internet technology, not only realized the innovation of IT technology but also A major revolution in the IT business model will bring unprecedented and profound changes to the information industry. The main purpose of this article is to study the collaborative management and control method of blockchain in a cloud computing environment. This article mainly uses the blockchain consensus algorithm to analyze and research the blocking technology in the logistics supply chain, and solves the supplier’s benefit formula step by step; also uses the CloudBTF algorithm, Max-min algorithm, FCFS of cloud computing Algorithm, and compare the efficiency and security of the three methods to get the most conducive to the collaborative management and control of the blockchain. The experimental results of this paper show that blockchain collaborative management in a cloud computing environment can greatly improve the security of massive data storage and the collaborative distribution of data. Among them, the use of cloud computing platform priority algorithms can improve system load balancing by up to 12%, while Using the cloud computing platform FCFS algorithm can improve system load balancing by up to 15%.

Power Management of Virtualized Cloud Computing Platform

2012 ◽  
Vol 35 (6) ◽  
pp. 1262 ◽  
Author(s):  
Ke-Jiang YE ◽  
Zhao-Hui WU ◽  
Xiao-Hong JIANG ◽  
Qin-Ming HE
Keyword(s):  
Cloud Computing ◽  
Power Management ◽  
Computing Platform ◽  
Cloud Computing Platform

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Node Failures

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

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