State detection method for cloud storage system based on compressive sensing

被引：1

作者：

Feng, Jing ^{[1
]}

Zhang, Liangliang ^{[1
]}

Shen, Ye ^{[1
]}

Liang, Luping ^{[2
]}

机构：

[1] Department of Meteorological and Hydrological Operations Command in Institute of Meteorology, PLA University of Science and Technology

[2] The 63rd Research Institute of PLA University of Science and Technology

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2013年 / 43卷 / 02期

关键词：

Cloud storage; Compressive sensing; SDCS(state detection with compressive sensing); Status detection; Status reconstruction;

D O I：

10.3969/j.issn.1001-0505.2013.02.013

中图分类号：

学科分类号：

摘要：

To solve the contradiction between precision and scale when dealing with large-scale data, by means of compressive sensing in signal processing which is superior to the Nyquist, a method called SDCS(state detection with compressive sensing) for detecting the state of cloud storage system is proposed. Based on the typical MP (matching pursuit) algorithm, this method is developed by adding the constraint condition that the sum of the rows in Bernoulli measurement matrix equals zero. The SDCS can measure sparse signals containing direct current component and ensure the equivalence between the improved target function and the original one. Then, this method is applied to state detection of FFS(formicary file system).The experimental results show that for the status signs with the sparse degree of 10, when the number of detection is more than 70, all the abnormal nodes can be fixed accurately and the compression ratio is 3.5%, indicating that this method can improve the efficiency of locating the abnormal nodes and satisfy the requirement of high precision detection for the large scale system.

引用

页码：296 / 300

页数：4

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