Digital twin based fault analysis in hybrid-cloud applications

Author:

Das Sankar N.1,Ahuja Manish1,Singi Kapil1,Dey Kuntal1,Kaulgud Vikrant1,Raman Mahesh V.2,Tung Teresa2

Affiliation:

1. Accenture Labs, India

2. Accenture, India

Publisher

ACM

Reference17 articles.

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2. M. S. Aktas . 2018. Hybrid cloud computing monitoring software architecture. Concurrency and Computation: Practice and Experience 30, 21 ( 2018 ). M. S. Aktas. 2018. Hybrid cloud computing monitoring software architecture. Concurrency and Computation: Practice and Experience 30, 21 (2018).

3. D. Anurag , M. Ahuja , S. Sikand , R. M. Rao , R. P. J. C. Bose , N. Dubash , and S. Podder . 2018. Identifying Implementation Bugs in Machine Learning Based Image Classifiers Using Metamorphic Testing . In Proc. of ISSTA 2018 . 118--128. D. Anurag, M. Ahuja, S. Sikand, R. M. Rao, R. P. J. C. Bose, N. Dubash, and S. Podder. 2018. Identifying Implementation Bugs in Machine Learning Based Image Classifiers Using Metamorphic Testing. In Proc. of ISSTA 2018. 118--128.

4. Deep digital twins for detection, diagnostics and prognostics

5. R. Buyya , S. N. Srirama , and G. Casale et al . 2018 . A manifesto for future generation cloud computing: Research directions for the next decade. ACM computing surveys 51, 5 (2018), 1--38. R. Buyya, S. N. Srirama, and G. Casale et al. 2018. A manifesto for future generation cloud computing: Research directions for the next decade. ACM computing surveys 51, 5 (2018), 1--38.

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