Adaptive replica consistency mechanism for cloud storage

doi:10.7523/j.issn.1002-1175.2013.01.014

Abstract

Abstract:

An adaptive replica consistency mechanism is proposed based on consistency requirements, including update consistency and reconciliation strategy. System regulates replica consistency mechanism dynamically according to consistency requirements so that a dynamic balance between consistency, availability, and system performance is achieved. Evaluation results show that the proposed mechanism decreases the amount of operation significantly while guarantees consistency requirements and promotes replica availability.

Key words: adaptive, replica, consistency, balance, cloud storage

CLC Number:

TP393

WANG Xi-Mei, YANG Shou-Bao, WANG Shu-Ling, GUO Liang-Min. Adaptive replica consistency mechanism for cloud storage[J]. , 2013, 30(1): 90-97.

References

[1] Asplund M, Nadjm-Tehrani S. Post-partition reconciliation protocols for maintaining consistency[C]//Proceedings of the 21st ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA, April 2006.

[2] Zhou X, Lu X L, Hou M S, et al. A dynamic distributed replica management mechanism based on accessing frequency detecting[J]. SIGOPS Oper Syst Rev, 2004, 38(3): 26-34.

[3] Chang R, Chang J. Adaptable replica consistency service for data grid[C]//Third International Conference on Information Technology: New Generations(ITNG'06). 2006.

[4] Cao D M. The research of replica selection and consistency for grid . Wuhan: Huazhong University of Science and Technology, 2007 (in Chinese). 曹冬梅. 基于网格环境的副本选择与一致性研究 . 武汉: 华中科技大学, 2007.

[5] Kraska T, Hentschel M, Alonso G, et al. Consistency rationing in the cloud: pay only when it matters[J]. Proceedings of the VLDB Endowment, 2009, 2(1): 253-264.

[6] Yu H F, Amin V. Design and evaluation of a conit-based continuous consistency model for replicated services[J]. ACM Transactions on Computer Systems,2002,20(3): 239-282.

[7] Asplund M, Nadjm-Tehrani S. Formalising reconciliation in partitionable networks with distributed services[C]//Butler M, et al. (Eds) Rigorous Development of Complex Fault-tolerant Systems (Lecture Notes in Computer Science, Vol. 4157). Springer: Berlin, 2006: 37-58.

[8] Beyer S, Ruiz-Fuertes M I, Galdamez P, et al. Exploiting commutativity for efficient replication in partitionable distributed systems[C]//Proceedings of the 2007 OTM Confederated International Conference on the Move to Meaningful Internet sSstems-Volume Part II. Vilamoura, Portugal, Springer-Verlag, 2007.

[9] Gilbert S, Lynch N. Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services[J]. ACM,2002, 33: 51-59.

[1]	YU Wenxin, LI Kai, ZHOU Mingtuo, LI Jian, YANG Yang. Adaptive neural network for 3D channel amplitude prediction [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 678-686.
[2]	LI Chuanjin, HU Shunjun. Diurnal variation of energy flux of Haloxylon ammodendron community in the southern margin of Gurbantunggut Desert under different weather conditions [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(4): 567-575.
[3]	SONG Zhihua, CHEN Kunshan, ZENG Jiangyuan, XU Zhen. A new adaptive filter for slow-moving target detection based on FMCW radar [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(3): 382-391.
[4]	CHEN Hongxing, LI Jiangyue, YANG Degang, LI Xiaohu, CAI Tianyi, XIA Fuqiang. Spatial distribution of agricultural production from perspective of water footprint: a case study of north-piedmont major agriculture production regions of Tianshan Mountains, Xinjiang [J]. Journal of University of Chinese Academy of Sciences, 2021, 38(2): 240-251.
[5]	ZHANG Shengnan, XU Yanbin, DONG Feng. Sparse regularization algorithm with adaptive shrinkage thresholding operator for image reconstruction [J]. , 2020, 37(2): 242-247.
[6]	FU Sheng, XUE Yuan, ZHANG Sanguo. Survey on angle-based classification [J]. , 2019, 36(3): 289-298.
[7]	JIANG Sha, QIU Xiaolan, HAN Bing, HU Wenlong, LU Xiaojun. A fast evaluation method of channel imbalance of polarimetric SAR based on common distributed targets [J]. , 2019, 36(2): 280-288.
[8]	WU Yuanyuan, SHEN Liyong. Imbalanced fuzzy multiclass support vector machine algorithm based on class-overlap degree undersampling [J]. , 2018, 35(4): 536-543.
[9]	CAI Shaoyang, CHEN Li, WANG Weidong. Design and experiments of an adaptive OFDM system for visible light communication [J]. , 2018, 35(1): 137-143.
[10]	ZHAO Hanqing, XU Zuwei, ZHAO Haibo. Predictions of compositional mixing degree in two-component aggregation [J]. , 2017, 34(2): 204-209.
[11]	PAN Zhigang, WANG Xiaolong, LI Zhiyong. An improved block adaptive quantization algorithm based on adaptive bit-allocation for SAR raw data compression [J]. , 2017, 34(1): 106-111.
[12]	HOU Jinzhong, ZHANG Libo, LUO Tiejian. A distributed storage method of balancing video resources [J]. , 2016, 33(5): 686-692.
[13]	LI Congwu, PAN Wuqiong, LIN Jingqiang. An AD-based private cloud storage access control scheme [J]. , 2015, 32(5): 676-681.
[14]	WANG Lei, WANG Pingjian, XIANG Ji. Unified authentication technology in cloud storage environment [J]. , 2015, 32(5): 682-688.
[15]	LIU Renlong, YANG Jiankui, XIONG Shifeng. Averaging-based truncated stochastic approximation algorithm [J]. , 2015, 32(3): 289-294.