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An adaptive switching scheme for iterative computing in the cloud

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Abstract

Delta-based accumulative iterative computation (DAIC) model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAIC model and the asynchronous DAIC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAIC models suffers fromthe serious network jitter and load jitter caused bymultitenancy in the cloud. In this paper, we develop a system, namely HybIter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAIC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.

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Authors and Affiliations

Authors

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Correspondence to Xiaofei Liao.

Additional information

Yu Zhang is now a PhD candidate in computer science and technology of Huazhong University of Science and Technology (HUST), Wuhan, China. His research interests include big data processing, cloud computing and distributed systems. His current topic mainly focuses on application-driven big data processing and optimizations.

Xiaofei Liao received his PhD degree in computer science and engineering from Huazhong University of Science and Technology (HUST), China in 2005. He is now a professor in school of Computer Science and Engineering at HUST. His research interests are in the areas of system virtualization, system software, and cloud computing.

Hai Jin received the BS, MA, and PhD degrees in computer engineering from the Huazhong University of Science and Technology (HUST) in 1988, 1991, and 1994, respectively. Currently, he is a professor of Computer Science and Engineering at HUST in China. He is currently the dean of School of Computer Science and Technology at HUST. His research interests include virtualization technology for computing system, cluster computing and grid computing, peer-to-peer computing, network storage, network security, and high-assurance computing. He is the member of grid forum steering group (GFSG). He is a senior member of the IEEE and a member of the ACM.

Li Lin is a PhD candidate in computer science and engineering from the Huazhong University of Science and Technology (HUST). He received the BS, MA degree in computer science and engineering from Sichuan University. He is also a lecturer in Fujian Normal University. His research interests are in the area of cloud gaming, mobile computing, and cloud-mobile computing fusion.

Feng Lu received her PhD degree in computer science and engineering from Huazhong University of Science and Technology (HUST), China. She is now an associate professor in the school of Computer Science and Engineering at HUST. Her main research interests include mobile internet, parallel computing, cloud service and social network.

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Zhang, Y., Liao, X., Jin, H. et al. An adaptive switching scheme for iterative computing in the cloud. Front. Comput. Sci. 8, 872–884 (2014). https://doi.org/10.1007/s11704-014-3472-4

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  • DOI: https://doi.org/10.1007/s11704-014-3472-4

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