Performance Analysis on Distributed Storage Systems in Ring Networks

2020 ◽  
pp. 1-1
Author(s):  
Shan Qu ◽  
Qin Zhang ◽  
Jinbei Zhang ◽  
Yuan Sun ◽  
Xinbing Wang
Keyword(s):  
Performance Analysis ◽  
Storage Systems ◽  
Distributed Storage ◽  
Ring Networks ◽  
Distributed Storage Systems

Analysis of a Stochastic Model of Replication in Large Distributed Storage Systems

2017 ◽  
Vol 45 (1) ◽  
pp. 51-51
Author(s):  
Wen Sun ◽  
Véronique Simon ◽  
Sébastien Monnet ◽  
Philippe Robert ◽  
Pierre Sens
Keyword(s):  
Stochastic Model ◽  
Storage Systems ◽  
Distributed Storage ◽  
Distributed Storage Systems

Tree-Structured Parallel Regeneration Based on Regenerating Codes for Multiple Data Losses in Distributed Storage Systems

2014 ◽  
Vol 918 ◽  
pp. 295-300
Author(s):  
Peng Fei You ◽  
Yu Xing Peng ◽  
Zhen Huang ◽  
Chang Jian Wang
Keyword(s):  
Storage Systems ◽  
Distributed Storage ◽  
Data Loss ◽  
Data Reliability ◽  
Data Redundancy ◽  
Regeneration Time ◽  
Multiple Data ◽  
Distributed Storage Systems ◽  
Regenerating Codes ◽  
Reliability And Availability

In distributed storage systems, erasure codes represent an attractive data redundancy solution which can provide the same reliability as replication requiring much less storage space. Multiple data losses happens usually and the lost data should be regenerated to maintain data redundancy in distributed storage systems. Regeneration for multiple data losses is expected to be finished as soon as possible, because the regeneration time can influence the data reliability and availability of distributed storage systems. However, multiple data losses is usually regenerated by regenerating single data loss one by one, which brings high entire regeneration time and severely reduces the data reliability and availability of distributed storage systems. In this paper, we propose a tree-structured parallel regeneration scheme based on regenerating codes (TPRORC) for multiple data losses in distributed storage systems. In our scheme, multiple regeneration trees based on regenerating code are constructed. Firstly, these trees are created independently, each of which dose not share any edges from the others and is responsible for one data loss; secondly, every regeneration tree based on regenerating codes owns the least network traffic and bandwidth optimized-paths for regenerating its data loss. Thus it can perform parallel regeneration for multiple data losses by using multiple optimized topology trees, in which network bandwidth is utilized efficiently and entire regeneration is overlapped. Our simulation results show that the tree-structured parallel regeneration scheme reduces the regeneration time significantly, compared to other regular regeneration schemes.

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