Load balancing strategy of cloud storage based on Hopfield neural network
LI Qiang1,2, LIU Xiaofeng3
1. College of Finance and Economics, Taiyuan University of Technology, Taiyuan Shanxi 030024, China; 2. College of Information, Shanxi Finance and Taxation College, Taiyuan Shanxi 030024, China; 3. College of Data Science, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
Abstract:Focusing on the shortcoming of low storage efficiency and high recovery cost after copy failure of the current Hadoop, Hopfield Neural Network (HNN) was used to improve the overall performance. Firstly, the resource characteristics that affect the storage efficiency were analyzed. Secondly, the resource constraint model was established, the Hopfield energy function was designed and simplified. Finally, the average utilization rate of 8 nodes was analyzed by using the standard test case Wordcount, and the performance and resource utilization of the proposed strategy were compared with three typical algorithms including dynamic resource allocation algorithm, energy-efficient algorithm and Hadoop default storage strategy, and the comparison results showed that the average efficiency of the storage strategy based on HNN was promoted by 15.63%, 32.92% and 55.92% respectively. The results indicate that the proposed algorithm can realize the resource load balancing, help to improve the storage capacity of Hadoop, and speed up the retrieval.
李强, 刘晓峰. 基于Hopfield神经网络的云存储负载均衡策略[J]. 计算机应用, 2017, 37(8): 2214-2217.
LI Qiang, LIU Xiaofeng. Load balancing strategy of cloud storage based on Hopfield neural network. Journal of Computer Applications, 2017, 37(8): 2214-2217.
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