大规模非结构化数据资源快速存储方法仿真

Simulation of Large-Scale Unstructured Data Resource Fast Storage Method

研究一种有效的数据资源快速存储方法,可以减少丢包率,保证存储的稳定性,提高数据存储速度,具备一定的应用价值。针对传统的非结构化数据资源快速存储模式存在能耗均衡性较差、存储延时较长和丢包率较高等问题,提出基于海量存储云调制的非结构化数据资源快速存储方法,该方法将多类别非结构化数据资源集合作为输入,对特征要素进行阈值处理,输出大规模非结构化数据资源关联模型。根据关联模型得到数据资源储存适应的关联强度,将强度与频宽占用概率进行非结构化数据资源储存控制,调节非结构化数据的粒度,依照最小传输粒度频宽梯度传输,加快大规模非结构化数据资源快速存储。通过实验结果验证,上述方法可降低存储延时、减少存储丢包率、保证存储能耗的均衡性,提高大规模非结构化数据资源的存储效率。

At present, it is necessary to research an effective method for fast storage of data resource. In this way, the packet loss rate can be reduced, and the stability of storage can be guaranteed, and the data storage speed can be improved. Therefore, this article focuses on a fast storage method for unstructured data resource based on massive storage cloud modulation. This method took the set of multi-class unstructured data resource as the input, and then the method performed threshold processing on the feature elements, and thus to output the large-scale unstructured data resource association model. According to the association model, the correlation intensity of data resource storage adaptation was obtained. Moreover, unstructured data resource storage control was performed on intensity and bandwidth occupation probability. Finally, the granularity of unstructured data was adjusted. According to the minimum transmission granularity bandwidth gradient, unstructured data was transmitted. Thus, the rapid storage for large-scale unstructured data resource was accelerated. Simulation results prove that the proposed method can reduce the storage delay, decrease the storage packet loss rate, and ensure the equilibrium of storage energy consumption. Meanwhile, this method can improve the storage efficiency of large-scale unstructured data resource.