Abstract
Abstract. The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.
Highlights
With the great-leap-forward development of remote sensing image acquisition ability, ground remote sensing system is faced with a bottleneck problem which is about the processing and interpretation and sharing of huge data
The new technology and processing strategy is needed to solve the problem of processing capacity of existing systems.Focusing on big data, a number of emerging data storage, data processing, data mining and analysis technologies have emerged, such as cloud computing, NoSQL, CloudDB, and distributed system infrastructure hadoop developed by the Apache Foundation
The data center processing technology is based on the optimization design of data storage management system, which can fully call all storage node devices and computing resources, establish a high-speed parallel processing mechanism in the
Summary
With the great-leap-forward development of remote sensing image acquisition ability, ground remote sensing system is faced with a bottleneck problem which is about the processing and interpretation and sharing of huge data. The above technologies and platforms make mass data processing more convenient, cheaper, and faster. It has been applied in ecommerce, mobile communications, and intelligent transportation. It supports parallel computing and is compatible with different hardware cluster. It can increase the efficiency of data parallel applications through optimizing the data storage management structure and fully using the cluster resource computing nodes. The data center processing technology is based on the optimization design of data storage management system, which can fully call all storage node devices and computing resources, establish a high-speed parallel processing mechanism in the
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
