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.
[1] BORTHAKUR D. Hadoop[EB/OL].[2011-06-15]. http://lucene.apache.org/hadoop. [2] BONVIN N, PAPAIOANNOU T G, ABERER K. A self-organized, fault-tolerant and scalable replication scheme for cloud storage[C]//SoCC' 10:Proceedings of the 1st ACM Symposium on Cloud Computing. New York:ACM, 2010:205-216. [3] WEI Q, VEERAVALLI B, GONG B, et al. CDRM:a cost-effective dynamic replication management scheme for cloud storage cluster[C]//CLUSTER' 10:Proceedings of the 2010 IEEE International Conference on Cluster Computing. Washington, DC:IEEE Computer Society, 2010:188-196. [4] RUAN X, XIE J, DING Z, et al. Improving MapReduce performance through data placement in heterogeneous Hadoop clusters[C]//Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum. Washington, DC:IEEE Computer Society, 2010:1-9. [5] WANG Z, LI T, XIONG N, et al. A novel dynamic network data replication scheme based on historical access record and proactive deletion[J]. The Journal of Supercomputing, 2012, 62(1):227-250. [6] 周敬利,周正达.改进的云存储系统数据分布策略[J].计算机应用,2012,32(2):309-312. (ZHOU J L, ZHOU Z D. Improved data distribution strategy for cloud storage system[J]. Journal of Computer Applications, 2012, 32(2):309-312.) [7] 林伟伟.一种改进的Hadoop数据放置策略[J].华南理工大学学报(自然科学版),2012,40(1):152-153. (LIN W W. An improved data placement strategy for Hadoop[J]. Journal of South China University of Technology (Natural Science Edition), 2012, 40(1):153-158.) [8] 张兴.基于Hadoop的云存储平台的研究与实现[D].成都:电子科技大学,2013:22-38. (ZHANG X. Research and Implementation of cloud storage plateform based on Hadoop[D]. Chengdu:University of Electronic Science and Technology of China, 2013:22-38.) [9] 付雄,贡晓杰,王汝传.云存储系统中基于分簇的数据复制策略[J].计算机工程与科学,2014,36(12):2296-2304. (FU X G, GONG X J, WANG R C. A cluster based data replication strategy in cloud storage systems[J]. Computer Engineering & Science, 2014, 36(12):2296-2304.) [10] 李强,刘晓峰,贺静.基于语音特征的情感分类[J].小型微型计算机系统,2016,37(2):385-388. (LI Q, LIU X F, HE J. Sentiment classification based on voice features[J]. Journal of Chinese Computer Systems, 2016, 37(2):385-388.) [11] 王万良,吴启迪,徐新黎.基于Hopfield神经网络的作业车间生产调度方法[J].自动化学报,2002,28(5):838-844. (WANG W L, WU Q D, XU X L. Hopfield neural network approach for job-shop scheduling problems[J]. Acta Automatica Sinica, 2002, 28(5):838-844.) [12] 王秀利,吴惕华.一种求解多处理器作业调度的Hopfield神经网络方法[J].系统工程与电子技术,2002,24(8):13-16. (WANG X L, WU T H. Scheduling multiprocessor job using Hopfield neural network[J]. Systems Engineering and Electronics, 2002, 24(8):13-16.) [13] HOPFIELD J J, TANK D W. Neural computation of decision in optimization problems[J]. Biological Cybernetics, 1985, 52(3):141-152.
2017, Vol. 37 
