An adaptive switching scheme for iterative computing in the cloud
Yu ZHANG, Xiaofei LIAO(), Hai JIN, Li LIN, Feng LU
Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology,Wuhan 430074, China
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%.
. [J]. Frontiers of Computer Science, 2014, 8(6): 872-884.
Yu ZHANG, Xiaofei LIAO, Hai JIN, Li LIN, Feng LU. An adaptive switching scheme for iterative computing in the cloud. Front. Comput. Sci., 2014, 8(6): 872-884.
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