分布式存储中的克隆piggybacking框架

doi:10.19665/j.issn1001-2400.2020.06.020

Abstract

Abstract:

With the rapid development of 5G technology, distributed storage systems are popular with its features such as low cost, high availability, high throughput and massive storage capacity. Due to the frequent node failures, it is important to adopt data fault tolerance technology to ensure the reliability of data. In recent years, the piggybacking framework has received extensive attention for its excellent repair properties. In this paper, we propose a cloned piggybacking framework design to optimize average repair bandwidth and repair locality. Different from other piggybacking frameworks, the piggybacking and repairing methods are carried out on one set of nodes. Then the corresponding nodes in other groups are piggybacked in the same way. The trade-off between repair bandwidth and repair locality is established by the idea of “one parity node only piggybacks symbols from one substripe”. Compared with the existing piggybacking framework, the cloned piggybacking framework is simple in design and can further reduce the repair bandwidth of information nodes.

Key words: abstract syntax tree, deep learning, recurrent neural network, vulnerability mining, distributed storage, piggybacking framework, repair bandwidth, repair locality

CLC Number:

TP311.5

ZHANG Lu,SUN Rong,LIU Jingwei. Cloned piggybacking framework for distributed storage[J].Journal of Xidian University, 2020, 47(6): 139-147.

Figures/Tables 7

piggybacking框架	子条带	平均修复带宽比率
RSR2^[11]	2r-3	$r + 1 2 r - 3$ $\stackrel{ r \to ∞ }{\longrightarrow}$ $12$
REPB^[12]	s'+p'	$2 r + 1$ $\stackrel{ r \to ∞ }{\longrightarrow}$0
SPB^[13]	r	$2 r - 1 r$ $\stackrel{ r \to ∞ }{\longrightarrow}$0
CPB	r	$r 3 + 61 r 2 - 155 r - 9 16 r 3$ $\stackrel{ r \to ∞ }{\longrightarrow}$0.062 5

References 17

[1]	李鑫, 孙蓉, 刘景伟. 分布式存储系统中容错技术综述[J]. 无线电通信技术, 2019, 45(5): 463-475.
	LI Xin, SUN Rong, LIU Jingwei. Overview of Fault-tolerant Techniques in Distributed Storage Systems[J]. Radio Communications Technology, 2019, 45(5): 463-475.
[2]	王意洁, 许方亮, 裴晓强. 分布式存储中的纠删码容错技术研究[J]. 计算机学报, 2017, 40(1): 236-255.
	WANG Yijie, XU Fangliang, PEI Xiaoqiang. Research on Erasure Code-Based Fault-Tolerant Technology for Distribute Storage[J]. Chinese Journal of Computers, 2017, 40(1): 236-255.
[3]	SATHIAMOORTHY M, ASTERIS M, PAPAILIOPOULOS D, et al. XORing Elephants: Novel Erasure Codes for Big Data[J]. Proceedings of the VLDB Endowment, 2013, 6(5): 325-336.
[4]	HUANG C, SIMITCI H, XU Y, et al. Erasure Coding in Windows Azure Storage[C]// Proceedings of the 2012 USENIX Annual Technical Conference. Berkeley: USENIX Association, 2012: 15-26.
[5]	郝晓慧, 车书玲, 张欣瑜. LRC码最小距离限的深入分析[J]. 西安电子科技大学学报, 2018, 45(5): 75-79+135.
	HAO Xiaohui, CHE Shuling, ZHANG Xinyu. In-depth Analysis of Bounds on the Minimum Distance of LRCs[J]. Journal of Xidian University, 2018, 45(5): 75-79+135.
[6]	王祥旭, 车书玲, 纪玉晖. 一种有效的局部可修复码的构造方法[J]. 西安电子科技大学学报, 2019, 46(3): 26-31.
	WAN GXiangxu, CHE Shuling, JI Yuhui. Effective Construction Method for Locally Repairable Codes[J]. Journal of Xidian University, 2019, 46(3): 26-31.
[7]	DIMAKIS A G, GODFREY P B, WU Y, et al. Network Coding for Distributed Storage Systems[J]. IEEE Transactions on Information Theory, 2010, 56(9): 4539-4551. doi: 10.1109/TIT.2010.2054295
[8]	ALRABIAH O, GURUSWAMI V. An Exponential Lower Bound on the Sub-packetization of MSR Codes[C]// Proceedings of the 2019 Annual ACM Symposium on Theory of Computing. New York: ACM, 2019: 979-985.
[9]	RASHMI K V, SHAH N B, RAMCHANDRAN K. A Piggybacking Design Framework for Read-and Download-Efficient Distributed Storage Codes[C]// Proceedings of the 2013 IEEE International Symposium on Information Theory. Piscataway: IEEE, 2013: 331-335.
[10]	RASHMI K V, SHAH N B, GU D, et al. A “Hitchhiker’s” Guide to Fast and Efficient Data Reconstruction in Erasure-coded Data Centers[J]. Computer Communication Review, 2015, 44(4): 331-342.
[11]	RASHMI K V, SHAH N B, RAMCHANDRAN K. A Piggybacking Design Framework for Read-and Download-efficient Distributed Storage Codes[C]// Proceedings of the 2017 IEEE Transactions on Information Theory. Piscataway: IEEE, 2017: 5802-5820.
[12]	YUAN S, HUANG Q. Generalized Piggybacking Codes for Distributed Storage Systems[C]// Proceedings of the 2016 IEEE Global Communications Conference. Piscataway: IEEE, 2016: 1-6.
[13]	SHANGGUAN C, GE G. A New Piggybacking Design for Systematic MDS Storage Codes[J]. Designs, Codes and Cryptography, 2019, 87(12): 2753-2770.
[14]	YANG B, TANG X, LI J. A Systematic Piggybacking Design for Minimum Storage Regenerating Codes[J]. IEEE Transactions on Information Theory, 2015, 61(11): 5779-5786.
[15]	LI G Y, LIN X, TANG X. An Efficient One-to-One Piggybacking Design for Distributed Storage Systems[J]. IEEE Transactions on Communications, 2019, 67(12): 8193-8205.
[16]	SUN R, LI X, ZHANG L, et al. Distributed Storage Codes Based on Double-layered Piggybacking Framework[J]. IEEE Access, 2020, 8: 150447-150464.
[17]	SHAH N B, RASHMI K V, KUMAR P V, et al. Distributed Storage Codes with Repair-by-transfer and Nonachievability of Interior Points on the Storage-bandwidth Tradeoff[J]. IEEE Transactions on Information Theory, 2012, 58(3): 1837-1852.

余数	m=0	m=1	m=2	m=3	m=4
r=4	0.640 6	0.620 4	0.562 5	0.526 9	不存在
r=5	0.552 0	0.563 6	0.554 2	0.552 6	0.556 1

Cloned piggybacking framework for distributed storage

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 17

Related Articles 15

Metrics

Comments

Recommended 10

piggybacking框架	RSR2^[11]	SPB^[13]	CPB
编码复杂度	(2r-3)kr	kr²	kr²
信息节点修复复杂度	(2r-3)k	kr	kr

[1]	DAI Mingjun,LI Xiaofeng,DENG Haiyan,CHEN Bin. Private information retrieval with low encoding/decoding complexity [J]. Journal of Xidian University, 2021, 48(6): 212-220.
[2]	LIU Jiawei,ZHANG Wenhui,KOU Xiaoli,LI Yanni. Harnessing adversarial examples via input denoising and hidden information restoring [J]. Journal of Xidian University, 2021, 48(6): 23-31.
[3]	LI Peng,FENG Cunqian,XU Xuguang,TANG Zixiang. Ballistic target fretting classification network based on Bayesian optimization [J]. Journal of Xidian University, 2021, 48(5): 139-148.
[4]	YAN Jia,CAO Yudong,REN Jiaxing,CHEN Donghao,LI Xiaohui. Deep asymmetric compression Hashing algorithm [J]. Journal of Xidian University, 2021, 48(5): 212-221.
[5]	NING Yang,DU Jianchao,HAN Shuo,YANG Chuankai. Fire segmentation based on the improved DeeplabV3+ and the analytical method for fire development [J]. Journal of Xidian University, 2021, 48(5): 38-46.
[6]	ZHOU Peng,YANG Jun. Semantic segmentation of remote sensing images based on neural architecture search [J]. Journal of Xidian University, 2021, 48(5): 47-57.
[7]	QI Yanjun,KONG Yueping,WANG Jiajing,ZHU Xudong. Gait recognition method combining LSTM and CNN [J]. Journal of Xidian University, 2021, 48(5): 78-85.
[8]	SONG Jianfeng,MIAO Qiguang,WANG Chongxiao,XU Hao,YANG Jin. Multi-scale single object tracking based on the attention mechanism [J]. Journal of Xidian University, 2021, 48(5): 110-116.
[9]	ZHANG Yuhao,CHENG Peitao,ZHANG Shuhao,WANG Xiumei. Lightweight image super-resolution with the adaptive weight learning network [J]. Journal of Xidian University, 2021, 48(5): 15-22.
[10]	HUI Haisheng,ZHANG Xueying,WU Zelin,LI Fenglian. Method for stroke lesion segmentation using the primary-auxiliary path attention compensation network [J]. Journal of Xidian University, 2021, 48(4): 200-208.
[11]	SUN Haojie,LI Miaoyu,ZHANG Panpan,XU Pengfei. Self-supervised facial asymmetry learning for automatic evaluation of facial paralysis [J]. Journal of Xidian University, 2021, 48(3): 115-122.
[12]	YANG Yanbo,ZHANG Jiawei,MA Jianfeng. Method for using the blockchain to protect data privacy of IoV [J]. Journal of Xidian University, 2021, 48(3): 21-30.
[13]	XU Bin,ZHANG Yongshun,ZHANG Qin,WANG Fuping,ZHENG Guimei. Radar HRRP target recognition based on the multiplicative RNN model [J]. Journal of Xidian University, 2021, 48(2): 49-54.
[14]	ZHANG Hua,GAO Haoran,YANG Xingguo,LI Wenmin,GAO Fei,WEN Qiaoyan. TargetedFool:an algorithm for achieving targeted attacks [J]. Journal of Xidian University, 2021, 48(1): 149-159.
[15]	YANG Hongyu,ZENG Renyun. Method for assessment of network security situation with deep learning [J]. Journal of Xidian University, 2021, 48(1): 183-190.