基于Hopfield神经网络的云存储负载均衡策略

针对当前Hadoop存储效率不高，且副本故障后恢复成本较高的问题，提出一种基于Hopfield神经网络（HNN）的存储策略。为了实现系统整体性能的提升，首先分析影响存储效率的资源特征；然后建立资源约束模型，设计Hopfield能量函数，并化简该能量函数；最后，通过标准用例Wordcount测试，分析8个节点的平均利用率，并与三个常用算法包括基于资源的动态调用算法、基于能耗的算法和Hadoop默认存储策略进行性能和资源利用方面的比较。实验表明，与对比算法相比，基于HNN的存储策略在效率上分别平均提升15.63%、32.92%和55.92%。因此，该方法在应用中可以更好地实现资源负载平衡，将有助于改善Hadoop的存储能力，并可以加快检索。

Load balancing strategy of cloud storage based on Hopfield neural network

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.