Efficient data-intensive processing in cloud computing environment

2014 ◽  
Author(s):  
Jiaman Ding ◽  
Yi Du ◽  
Qingxin Wang ◽  
Ying Jiang
Keyword(s):  
Cloud Computing ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Data Intensive ◽  
Efficient Data ◽  
Intensive Processing

Software Asset Re-Use

2012 ◽  
pp. 1-27
Author(s):  
Richard Millham
Keyword(s):  
Cloud Computing ◽  
Software As A Service ◽  
Legacy Systems ◽  
Computing Environment ◽  
Migration Process ◽  
Legacy System ◽  
Cloud Computing Environment ◽  
Data Intensive ◽  
System Migration ◽  
The Web

In this chapter, the author examines the migration process of a legacy system, as a software-as-a-service model, to the Web, and he looks at some of the reasons that drive this legacy system migration. As migration is often a multi-step process, depending on the legacy system being migrated, the author outlines several techniques and transformations for each step of the migration process in order to enable legacy systems, of different types, to be migrated to the cloud. Of particular interest are the different methods to handle data-intensive legacy systems to enable them to function in a cloud computing environment with reduced bandwidth. Unlike the migration of an unstructured legacy system to a locally-distributed desktop system, system migration to a cloud computing environment poses some unique challenges such as restricted bandwidth, scalability, and security. Part of this migration process is adapting the transformed legacy system to be able to function in such an environment. At the end of the chapter, several small case studies of legacy systems, each of a different nature successfully migrated to the cloud, will be given.

scholarly journals An efficient data security mechanism with data sharing and authentication

2020 ◽  
Vol 5 (19) ◽  
pp. 26-31
Author(s):  
Md. Farooque ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Cloud Computing ◽  
Data Sharing ◽  
Data Security ◽  
Security Analysis ◽  
Key Generation ◽  
Security Mechanism ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Efficient Data

In this paper an efficient security mechanism has been adopted for the cloud computing environment. It also provides an extendibility of cloud computing environment with big data and Internet of Things. AES-256 and RC6 with two round key generation have been applied for data and application security. Three-way security mechanism has been adopted and implemented. It is user to user (U to U) for data sharing and inter cloud communication. Then user to cloud (U to C) for data security management for application level hierarchy of cloud. Finally, cloud to user (C to U) for the cloud data protection. The security analysis has been tested with different iterations and rounds and it is found to be satisfactory.

Efficient Data Mining Model Design for a Large Database in the Cloud Computing Environment

2015 ◽  
Vol 713-715 ◽  
pp. 2447-2450
Author(s):  
Zhan Kun Zhao
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Manifold Learning ◽  
Model Design ◽  
Large Database ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Efficient Data ◽  
Computing Environments ◽  
Mining Model

Efficient data mining model design for a large database in the cloud computing environment is studied. For large databases efficiently mining problem, an efficient data mining model in the cloud computing environment based on improved manifold learning algorithms is proposed. The use of nonlinear manifold learning algorithms is able to reduce dimensionality of data vector feature in cloud computing environments, through characteristic extraction module to preprocess data, improved classical manifold learning algorithm is adopted to increase the distance between the data of sample spread intensive area and shorten the distance between the data of sample spread sparse area, prompting even overall distribution of sample database under cloud computing environment, so as to achieve accurate mining for efficient data in cloud computing environment. The experimental results show that the proposed method can accurately mine target data under cloud computing environments, with high efficiency and precision.

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