Software-Defined Storage (SDS) for Storage Virtualization

Cloud computing is the theoretical basis for future computing. All the global frameworks are now looking up to architecture which is purely based on cloud. Being the core of such a large web of network, it is important to consider the security aspects in a cloud based computing environment. This has resulted in a new research trend on the security issues of cloud. Cloud is a popular paradigm with extreme abilities and benefits for trending ICT environment. On the other end the major concern came in terms of security and privacy while adopting the cloud technology. This article is an effort to cover the challenges in fields like storage, virtualization and communication in cloud .Also it is a try to elaborate relevance of current cryptographic approach in order to increase security of cloud in ICT.

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An Improved Ec2s2 Framework for Secure Storage Virtualization

International Journal of Computer Applications ◽

10.5120/ijca2016910108 ◽

2016 ◽

Vol 144 (2) ◽

pp. 10-15

Author(s):

Gurvir Kaur ◽

Gagandeep Kaur

Keyword(s):

Storage Virtualization ◽

Secure Storage

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Development of Home Network Storage and Home Storage Virtualization

Digital Storage in Consumer Electronics ◽

10.1016/b978-075068465-1.50010-2 ◽

2008 ◽

pp. 201-224

Author(s):

Thomas M. Coughlin

Keyword(s):

Home Network ◽

Network Storage ◽

Storage Virtualization

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Monitoring of a Grid Storage Virtualization Service

International Journal of Grid and High Performance Computing ◽

10.4018/jghpc.2013010104 ◽

2013 ◽

Vol 5 (1) ◽

pp. 53-69

Author(s):

Jacques Jorda ◽

Aurélien Ortiz ◽

Abdelaziz M’zoughi ◽

Salam Traboulsi

Keyword(s):

Monitoring System ◽

Data Storage ◽

Large Scale ◽

Distributed Storage ◽

Storage System ◽

Data Access ◽

Data Placement ◽

Workload Prediction ◽

Storage Virtualization

Grid computing is commonly used for large scale application requiring huge computation capabilities. In such distributed architectures, the data storage on the distributed storage resources must be handled by a dedicated storage system to ensure the required quality of service. In order to simplify the data placement on nodes and to increase the performance of applications, a storage virtualization layer can be used. This layer can be a single parallel filesystem (like GPFS) or a more complex middleware. The latter is preferred as it allows the data placement on the nodes to be tuned to increase both the reliability and the performance of data access. Thus, in such a middleware, a dedicated monitoring system must be used to ensure optimal performance. In this paper, the authors briefly introduce the Visage middleware – a middleware for storage virtualization. They present the most broadly used grid monitoring systems, and explain why they are not adequate for virtualized storage monitoring. The authors then present the architecture of their monitoring system dedicated to storage virtualization. We introduce the workload prediction model used to define the best node for data placement, and show on a simple experiment its accuracy.

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