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Open AccessArticle

A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments

1
Department of Game & Multimedia Engineering, Korea Polytechnic University, Siheung-si, Gyeonggi-do 15073, Korea
2
School of Information Technology Engineering, Daegu Catholic University, Gyeongsan-si, Gyeongsangbuk-do 38430, Korea
3
Department of Computer Science & Engineering, Korea University, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2018, 10(1), 30; https://doi.org/10.3390/sym10010030
Received: 15 November 2017 / Revised: 13 January 2018 / Accepted: 15 January 2018 / Published: 17 January 2018
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
Many artificial intelligence applications often require a huge amount of computing resources. As a result, cloud computing adoption rates are increasing in the artificial intelligence field. To support the demand for artificial intelligence applications and guarantee the service level agreement, cloud computing should provide not only computing resources but also fundamental mechanisms for efficient computing. In this regard, a snapshot protocol has been used to create a consistent snapshot of the global state in cloud computing environments. However, the existing snapshot protocols are not optimized in the context of artificial intelligence applications, where large-scale iterative computation is the norm. In this paper, we present a distributed snapshot protocol for efficient artificial intelligence computation in cloud computing environments. The proposed snapshot protocol is based on a distributed algorithm to run interconnected multiple nodes in a scalable fashion. Our snapshot protocol is able to deal with artificial intelligence applications, in which a large number of computing nodes are running. We reveal that our distributed snapshot protocol guarantees the correctness, safety, and liveness conditions. View Full-Text
Keywords: snapshot protocol; artificial intelligence; cloud computing; iterative computation snapshot protocol; artificial intelligence; cloud computing; iterative computation
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MDPI and ACS Style

Lim, J.; Gil, J.-M.; Yu, H. A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments. Symmetry 2018, 10, 30. https://doi.org/10.3390/sym10010030

AMA Style

Lim J, Gil J-M, Yu H. A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments. Symmetry. 2018; 10(1):30. https://doi.org/10.3390/sym10010030

Chicago/Turabian Style

Lim, JongBeom; Gil, Joon-Min; Yu, HeonChang. 2018. "A Distributed Snapshot Protocol for Efficient Artificial Intelligence Computation in Cloud Computing Environments" Symmetry 10, no. 1: 30. https://doi.org/10.3390/sym10010030

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