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Single Disk Failure recovery for X-Code-Based Parallel Storage Systems

IEEE TRANSACTIONS ON COMPUTERS 2014年第4期63卷 995-1007页

作者： Xu, Silei Li, Runhui Lee, Patrick P. C. Zhu, Yunfeng Xiang, Liping Xu, Yinlong Lui, John C. S. Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

In modern parallel storage systems (e. g., cloud storage and data centers), it is important to provide data availability guarantees against disk (or storage node) failures via redundancy coding schemes. One coding scheme is X-code, which is double-fault tolerant while achieving the optimal update complexity. When a disk/node fails, recovery must be carried out to reduce the possibility of data unavailability. We propose an X-code-based optimal recovery scheme called minimum-disk-read-recovery (MDRR), which minimizes the number of disk reads for single-disk failure recovery. We make several contributions. First, we show that MDRR provides optimal single-disk failure recovery and reduces about 25 percent of disk reads compared to the conventional recovery approach. Second, we prove that any optimal recovery scheme for X-code cannot balance disk reads among different disks within a single stripe in general cases. Third, we propose an efficient logical encoding scheme that issues balanced disk read in a group of stripes for any recovery algorithm (including the MDRR scheme). Finally, we implement our proposed recovery schemes and conduct extensive testbed experiments in a networked storage system prototype. Experiments indicate that MDRR reduces around 20 percent of recovery time of the conventional approach, showing that our theoretical findings are applicable in practice.

关键词： Parallel storage systems coding theory data availability recovery algorithm

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Performance analysis of compressed sensing with uniform scalar quantisation

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INTERNATIONAL JOURNAL OF ELECTRONICS 2014年第8期101卷 1042-1055页

作者： Lu, Wei Wang, Desheng Liu, Jian Liu, Yingzhuang Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China

In compressed sensing, the sampling rate can be greatly reduced based on the sparsity of the original signal;however, quantisation is one key aspect in signal acquirement. This article studies the scalar quantisation and its impacts on the compressed sensing. We consider the case in which both the original and the measurement signals are quantised or discrete, which widely exists in the practical applications such as the finite precision signals in the computer and the finite-set original signal in communication systems. We derive the recovery probability bound and the necessary measurement number bound for compressed sensing with scalar quantisation and simulate three modified compressed sensing recovery algorithms. Simulations show that coarse quantisation can be applied in compressed sensing for discrete or quantised sparse signal with sampling and storage efficiency.

关键词： compressed sensing scalar quantisation recovery algorithm measurement bound error probability

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Wavefront recovery Fourier-based algorithm used in a Vectorial Shearing Interferometer

Wavefront recovery Fourier-based algorithm used in a Vectori...

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Conference on Infrared Remote Sensing and Instrumentation XXI

作者： Bravo-Medina, Beethoven Garcia-Torales, G. Legarda-Saenz, Ricardo Flores, Jorge L. Univ Guadalajara Dept Elect Guadalajara 44840 Jalisco Mexico

ISBN: (纸本)9780819497178

Vectorial Shearing Interferometer is able to select variable shear and tilt along any wavefront direction. This system is self-referenced and with variable sensibility. We proposed this instrument to measure a phase object without rotational symmetry. Phase recovery is implemented by a Fourier-based algorithm and spatial unwrapping methods. We show results emphasizing the advantage in the easier selection of fringe density and directional derivative orientation for a specific optical element.

关键词： wavefront shearing interferometer recovery algorithm

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Structured Sparse Approximation via Generalized Regularizers: With Application to V2V Channel Estimation

Structured Sparse Approximation via Generalized Regularizers...

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IEEE Global Communications Conference (GLOBECOM)

作者： Beygi, Sajjad Strom, Erik G. Mitra, Urbashi USC Sch Elect Engn Los Angeles CA 90089 USA Chalmers Univ Technol Dept Signals & Syst Gothenburg Sweden

ISBN: (纸本)9781479935123

In this paper, we consider the estimation of a signal that has both group-and element-wise sparsity (joint sparsity);motivated by channel estimation in vehicle-to-vehicle channels. A general approach for the design of separable regularizing functions is proposed to adaptively induce sparsity in the estimation. A joint sparse signal estimation problem is formulated via these regularizers and its optimal solution is computed based on proximity operations. Our optimization results are quite general and they can be applied in the context of hierarchical sparsity models as well. The proposed recovery algorithm is a nested iterative method based on the alternating direction method of multipliers (ADMM). Due to regularizer separability, key operations can be performed in parallel. V2V channels are estimated by exploiting the joint sparsity (group/element-wise) exhibited in the delay-Doppler domain. Simulation results reveal that the proposed method can achieve as much as a 1 0 dB gain over previously examined methods.

关键词： channel estimation iterative methods mobile radio optimisation signal processing ADMM V2V channel estimation alternating direction method of multipliers delay-Doppler domain element-wise sparsity hierarchical sparsity models nested iterative method recovery algorithm separable regularizing functions sparse signal estimation problem structured sparse approximation vehicle-to-vehicle channels Approximation methods Channel estimation Estimation Joints Optimization Signal processing Vectors

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A survey on recent advances in speech compressive sensing

A survey on recent advances in speech compressive sensing

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IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing (iMac4s)

作者： Shukla, Urvashi P. Patel, Niteen B. Joshi, Amit M. SCET EC Surat India SVNIT EC Res Scholar Surat India

ISBN: (纸本)9781467350907;9781467350891

Compressive sensing (CS) is one of the upcoming fields which have paved its way for different approaches towards the signal acquisition and processing systems. In past years, many fields of application have emerged, where speech is one of the most popular one. Due to stochastic nature of speech signal, we are still in search for a proper sparse representation of the signal, so that we could easily incorporate CS and thereby reduce the increasing burden on the ADC. In this paper, we discuss some of the works which have been carried out towards the sparse representation of the speech signals. The idea is to concatenate them on a common platform by exploiting the signal and its basis matrices. Each of the approaches has its own pros and corns for the sparse representation.

关键词： Compressive sensing data acquisition recovery algorithm sparsity speech synthesis

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An Improved Compressive Sensing Image Fusion algorithm Based on NSCT Transform

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2nd International Conference on Mechatronics and Control Engineering (ICMCE 2013)

作者： Wu, Yanhai Yan, Di Ma, Mengxin Wu, Nan Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China Datang Mobile Xian Commun Equipment Co Xian 710061 Peoples R China Shaanxi Inst Educ Xian 710061 Peoples R China

ISBN: (纸本)9783037858943

A modified compressive sensing image fusion algorithm is proposed in this paper that is based on the NSCT transform. The algorithm is improved by introducing the theory of compressive sensing into image fusion that uses the NSCT transform to make a specific image be sparse on which only the high frequency coefficient is specifically measured;The improved algorithm then process the image fusion by retrieving the maximal value of the gradient of the neighborhood average from the measured high frequency coefficient, and accordingly, maximizing the absolute value of the neighborhood variance to the low-frequency counterpart. Afterwards, the improved algorithm can reconfigure the fusion image by using the MSP reconfiguration algorithm with final deliverable of the fusion image by committing to the NSCT reverse transform. Simulation results show that the improved algorithm is superior to other hand-on algorithms both in visual effect and in objective evaluation. In the case that the storage and transmission data are limited, the algorithm comes forth better effect of image fusion that is verified to be possesses of high value in practice.

关键词： Compressive Sensing Image Fusion NSCT Fusion Rules recovery algorithm

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Second-Level algorithms, Superrecursivity, and recovery Problem in Distributed Systems

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THEORY OF COMPUTING SYSTEMS 2012年第4期50卷 694-705页

作者： Burgin, Mark Gupta, Bidyut So Illinois Univ Dept Comp Sci Carbondale IL 62901 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

In this paper, we analyze network recovery algorithms, which allow computer networks to properly function in spite of failures. In this analysis, we use methods and tools of the theory of super-recursive algorithms. The concept of algorithm of the second level is introduced and studied. It is demonstrated that although the main components of various check-point/recovery algorithms are recursive algorithms, check-point/recovery algorithms, as a whole, are super-recursive second-level algorithms. Treating network recovery algorithms as second level algorithms is oriented at developing more powerful algorithms by combining existing ones in a common schema.

关键词： Distributed systems Network recovery recovery algorithm Check-point algorithm Super-recursive algorithm Second level algorithm

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2D sparse signal recovery via 2D orthogonal matching pursuit

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Science China(Information Sciences) 2012年第4期55卷 889-897页

作者： HUANG BorMin Space Science and Engineering Center University of Wisconsin-MadisonMadisonWI 53706USA

recovery algorithms play a key role in compressive sampling (CS).Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal,while many practical signals are two-dimensional (2D).By utilizing 2D separable sampling,2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms,*** matching pursuit (OMP),can be applied ***,even with 2D separable sampling,the memory usage and complexity at the decoder are still *** paper develops a novel recovery algorithm called 2D-OMP,which is an extension of *** the 2D-OMP,each atom in the dictionary is a *** each iteration,the decoder projects the sample matrix onto 2D atoms to select the best matched atom,and then renews the weights for all the already selected atoms via the least *** show that 2D-OMP is in fact equivalent to 1D-OMP,but it reduces recovery complexity and memory usage ***’s more important,by utilizing the same methodology used in this paper,one can even obtain higher dimensional OMP (say 3D-OMP,etc.) with ease.

关键词： compressive sampling 2D sparse signal recovery algorithm orthogonal matching pursuit

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GPU Implementation of Orthogonal Matching Pursuit for Compressive Sensing

GPU Implementation of Orthogonal Matching Pursuit for Compre...

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17th IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fang, Yong Chen, Liang Wu, Jiaji Huang, Bormin NW A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian Shaanxi Peoples R China Univ Wisconsin Space Sci & Engn Ctr Madison WI USA

ISBN: (纸本)9780769545769

recovery algorithms play a key role in compressive sampling (CS). Currently, a popular recovery algorithm for CS is the orthogonal matching pursuit (OMP), which possesses the merits of low complexity and good recovery quality. Considering that the OMP involves massive matrix/vector operations, it is very suited to being implemented in parallel on graphics processing unit (GPU). In this paper, we first analyze the complexity of each module in the OMP and point out the bottlenecks of the OMP lie in the projection module and the least-squares module. To speedup the projection module, Fujimoto's matrix-vector multiplication algorithm is adopted. To speedup the least-squares module, the matrix-inverse-update algorithm is adopted. Experimental results show that +40x speedup is achieved by our implementation of OMP on GTX480 GPU over on Intel(R) Core(TM) i7 CPU. Since the projection module occupies more than 2/3 of the total run time, we are looking for a faster matrix-vector multiplication algorithm.

关键词： compressive sampling recovery algorithm orthogonal matching pursuit graphics processing unit

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A Hybrid Approach to Failed Disk recovery Using RAID-6 Codes: algorithms and Performance Evaluation

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ACM TRANSACTIONS ON STORAGE 2011年第3期7卷 11-11页

作者： Xiang, Liping Xu, Yinlong Lui, John C. S. Chang, Qian Pan, Yubiao Li, Runhui Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China

The current parallel storage systems use thousands of inexpensive disks to meet the storage requirement of applications. Data redundancy and/or coding are used to enhance data availability, for instance, Row-diagonal parity (RDP) and EVENODD codes, which are widely used in RAID-6 storage systems, provide data availability with up to two disk failures. To reduce the probability of data unavailability, whenever a single disk fails, disk recovery will be carried out. We find that the conventional recovery schemes of RDP and EVENODD codes for a single failed disk only use one parity disk. However, there are two parity disks in the system, and both can be used for single disk failure recovery. In this article, we propose a hybrid recovery approach that uses both parities for single disk failure recovery, and we design efficient recovery schemes for RDP code (RDOR-RDP) and EVENODD code (RDOR-EVENODD). Our recovery scheme has the following attractive properties: (1) "read optimality" in the sense that our scheme issues the smallest number of disk reads to recover a single failed disk and it reduces approximately 1/4 of disk reads compared with conventional schemes;(2) "load balancing property" in that all surviving disks will be subjected to the same (or almost the same) amount of additional workload in rebuilding the failed disk. We carry out performance evaluation to quantify the merits of RDOR-RDP and RDOR-EVENODD on some widely used disks with DiskSim. The offline experimental results show that RDOR-RDP and RDOR-EVENODD outperform the conventional recovery schemes of RDP and EVENODD codes in terms of total recovery time and recovery workload on individual surviving disk. However, the improvements are less than the theoretical value (approximately 25%), as RDOR-RDP and RDOR-EVENODD change the disk access pattern from purely sequential to a more random one compared with their conventional schemes.

关键词： algorithms Reliability Theory Disk failure EVENODD code RAID recovery RDP code recovery algorithm

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