Problems of Network Data Storage and Cloud Databases

2019 ◽  
Author(s):  
S.Mubariz Khalilov ◽  
Irada B. Dadashova
Keyword(s):  
Data Storage ◽  
Network Data ◽  
Cloud Databases

Research of the Construction of Network Data Storage in the New Period

2012 ◽  
Vol 532-533 ◽  
pp. 1248-1252
Author(s):  
Xu Fang
Keyword(s):  
Data Storage ◽  
Network Data ◽  
New Period ◽  
Push Forward ◽  
The New Period

Nowadays the number of the users of the network data storage is becoming more and more. One of the problems we are confronted with is that how to construct our network data storage in the new period. Several typical network data storage is analyzed in the paper and the problems are analyzed .Some advice is put forward about the construction of the network data storage .We hope that the construction of our country’s network data storage can be push forward constantly through our effort.

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

Parallel Route Optimization Algorithm of Central Guidance

2013 ◽  
Vol 380-384 ◽  
pp. 1571-1575
Author(s):  
Hong Chen ◽  
Hu Xing Zhou ◽  
Juan Meng
Keyword(s):  
Parallel Computing ◽  
Real Time ◽  
Data Storage ◽  
Route Optimization ◽  
Guidance System ◽  
Network Data ◽  
Traffic Network ◽  
Computing Technique ◽  
The Real ◽  
Guangzhou City

To solve the problem that the central guidance system takes too long time to calculate the shortest routes between all node pairs of network which can not meet the real-time demand of central guidance, this paper presents a central guidance parallel route optimization method based on parallel computing technique involving both route optimization time and travelers preferences by means of researching three parts: network data storage based on an array, multi-level network decomposition with travelers preferences considered and parallel shortest route computing of deque based on messages transfer. And based on the actual traffic network data of Guangzhou city, the suggested method is verified on three parallel computing platforms including ordinary PC cluster, Lenovo server cluster and HP workstations cluster. The results show that above three clusters finish the optimization of 21.4 million routes between 5631 nodes of Guangzhou city traffic network in 215, 189 and 177 seconds with the presented method respectively, which can completely meet the real-time demand of the central guidance.

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