YML-PC: A Reference Architecture Based on Workflow for Building Scientific Private Clouds

Shang, Ling; Petiton, Serge G.; Emad, Nahid; Yang, Xiaolin

doi:10.1007/978-1-84996-241-4_9

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…The extension version of YML is able to manage at the run-time several middleware back-ends, achieving a dynamic federation of computing middleware [4]. We also extend the framework to be middleware for cloud computing platform [5,6].…”

Section: Introductionmentioning

confidence: 98%

Cryptographic Cloud Storage with Public Verifiability: Ensuring Data Security of the YML Framework

Lv¹,

Xu²,

Petiton³

2013

Intelligent Automation & Soft Computing

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YML framework is a well-adapted advanced tool to support designing and executing portable parallel applications over large scale peer to peer and grid middlewares. This work studies the problem of ensuring data security of the YML framework. We define and construct a mechanism that enables us to move the data repository to a public cloud infrastructure where the service provider is not completely trustworthy. To achieve confidentiality, we encrypt the data using the encryption algorithm in our prior work before uploading to the cloud, and then attach pre-classified keywords to them for ciphertext-searching, which are generated by a statistically consistent public-key encryption with keyword search (PEKS) scheme, so the service provider can use the corresponding trapdoor to identify all data containing some specific keywords without learning anything else. To ensure integrity, an elegant verification scheme is proposed, enabling a third party auditor (TPA), on behalf of data owner, to verify the integrity of the (encrypted) data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for cloud computing.

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Section: Introductionmentioning

confidence: 98%

Cryptographic Cloud Storage with Public Verifiability: Ensuring Data Security of the YML Framework

Lv¹,

Xu²,

Petiton³

2013

Intelligent Automation & Soft Computing

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“…Owing to the low costs and simple operation of cloud services, more and more scientific computing tasks are delivered to cloud platforms to complete. Scientific computing requires an ever-increasing number of computing resources to deliver for growing sizes of problems in a reasonable time frame and cloud computing holds promise for the performance-hungry scientific community [3]. Today, many open cloud research platforms have emerged, such as Chameleon Cloud [4].…”

Section: Introductionmentioning

confidence: 99%

Protecting scientific workflows in clouds with an intrusion tolerant system

Wang

Guo

et al. 2020

IET Information Security

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“…YML-PC [ 25 ] is a workflow based private cloud environment for scientific computing. It harnesses dedicated computing resources and volunteer computing resources.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the shortage from the volatility of the desktop grid environment, a trust model [ 26 ] is introduced in the study. Experiments from paper [ 25 ] testify that data dependent based DAG tasks can be dealt with in desktop grid environment.…”

Section: Introductionmentioning

confidence: 99%

Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing

Shang

Gao

et al. 2014

The Scientific World Journal

Self Cite

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This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications.

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YML-PC: A Reference Architecture Based on Workflow for Building Scientific Private Clouds

Cited by 6 publications

References 3 publications

Cryptographic Cloud Storage with Public Verifiability: Ensuring Data Security of the YML Framework

Cryptographic Cloud Storage with Public Verifiability: Ensuring Data Security of the YML Framework

Protecting scientific workflows in clouds with an intrusion tolerant system

Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing

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