Grid Architecture Storage — Utilising Grid Computing for Dynamic Data Storage

2005 ◽  
Author(s):  
P. Koszek ◽  
K. Sandrasegaran
Keyword(s):  
Grid Computing ◽  
Data Storage ◽  
Dynamic Data ◽  
Grid Architecture

Grid Computing and its Application to Geoinformatics

2010 ◽  
pp. 213-221
Author(s):  
Aijun Chen ◽  
Liping Di ◽  
Yuqi Bai ◽  
Yaxing Wei
Keyword(s):  
Grid Computing ◽  
Earth Science ◽  
Grid Architecture ◽  
Research Areas ◽  
Computing Grid ◽  
Open Geospatial Consortium ◽  
Definition Of ◽  
Hourglass Model ◽  
Geospatial Services ◽  
Protocol Architecture

The definition of the Grid computing and its application to geoinformatics are introduced. Not only the comparison of power Grid and computing Grid is illustrated, also Web technology and Grid technology are compared. The Hourglass Model of Grid architecture is depicted. The layered Grid architecture, relating to Internet protocol architecture, consists of the fabric (computer, storage, switches, etc.) layer, connectivity layer, resource layer, collective layer, and application layer. Grid computing has been applied to many disciplines and research areas, such as physics, Earth science, astronomy, bioinformatics, etc. By applying the Grid computing to Open Geospatial Consortium, Inc.’s Web services and geospatial standards from International Organization for Standardization, US Federal Geographic Data Committee and US NASA, a geospatial Grid is proposed here, which consisting of Grid-managed geospatial data and Grid-enabled geospatial services.

Identity-Based Dynamic Data Auditing for Big Data Storage

2019 ◽  
pp. 1-1
Author(s):  
Tao Shang ◽  
Feng Zhang ◽  
Xingyue Chen ◽  
Jianwei Liu ◽  
Xinxi Lu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Dynamic Data ◽  
Identity Based ◽  
Data Auditing ◽  
Big Data Storage

scholarly journals A note on dynamic data storage in FORTRAN IV

1975 ◽  
Vol 18 (4) ◽  
pp. 342-343 ◽  
Author(s):  
A. Chung-Phillips
Keyword(s):  
Data Storage ◽  
Dynamic Data

Effective Data Storage and Dynamic Data Auditing Scheme for Providing Distributed Services in Federated Cloud

2020 ◽  
Vol 29 (16) ◽  
pp. 2050259
Author(s):  
A. M. Sermakani ◽  
D. Paulraj
Keyword(s):  
Data Storage ◽  
Input Data ◽  
Data Access ◽  
Retrieval Algorithm ◽  
Public Cloud ◽  
Dynamic Data ◽  
Storage And Retrieval ◽  
Cloud Database ◽  
Private And Public ◽  
Data Auditing

Recently, many organizations and industries are using the cloud computing technologies for exchanging the resources and their confidential data. For this purpose, many cloud services are available and also provide the facility to categorize their users as private and public users for accessing their own data from private cloud and public cloud. The combination of these two clouds is called federated cloud which facilitates to allow both kinds of cloud users for accessing their own data on same cloud database. In this scenario, the authorization and authentication process is becoming complex task on cloud. For providing the facility to access their own data only from federated cloud, a new secured data storage and retrieval algorithm called AES and Triple-DES-based Secured Storage and Retrieval Algorithm (ATDSRA) are proposed for storing the private and public cloud user’s data securely on cloud database. Here, the TDES is used for encrypting the input data, data merging and aggregation methods were used for grouping the encrypted input data. Moreover, a new dynamic data auditing scheme called CRT-based Dynamic Data Auditing Algorithm (CRTDDA) is proposed for conducting the cloud data auditing over the federated cloud and also restricting the data access. The proposed new auditing mechanism that is able to protect the stored data from access violence. In addition, the standard Table64 is used for encryption and decryption processes. The experimental results of this work proves the efficiency of the proposed model in terms of security level.

Grid Computing for Ontology Matching

2012 ◽  
pp. 1752-1763
Author(s):  
Axel Tenschert
Keyword(s):  
Grid Computing ◽  
Ontology Matching ◽  
Distributed Environment ◽  
Grid Environment ◽  
Grid Architecture ◽  
High Efficient ◽  
Distributed Ontology

This chapter is examines the challenge of ontology matching in a grid environment in a scalable and high efficient way. For this, ontology matching approaches as well as grid computing are considered with the aim to present an approach for ontology matching on various resources. Hence, related approaches and tools are presented and discussed in order to provide an adequate background. Through this, a distributed ontology matching as it is required for ontology matching in a distributed environment such as the grid becomes usable. However, a novel ontology matching approach which meets the requirements of a grid architecture is considered in this chapter.

Dynamic data storage in FORTRAN

1990 ◽  
Vol 67 (6) ◽  
pp. 500
Author(s):  
Alice Chung-Phillips
Keyword(s):  
Data Storage ◽  
Dynamic Data
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