DocumentCode :
1791702
Title :
STORE: Data recovery with approximate minimum network bandwidth and disk I/O in distributed storage systems
Author :
Tai Zhou ; Hui Li ; Bing Zhu ; Yumeng Zhang ; Hanxu Hou ; Jun Chen
Author_Institution :
Shenzhen Eng. Lab. of Converged Networks Technol., Peking Univ., Shenzhen, China
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
33
Lastpage :
38
Abstract :
Recently, traditional erasure codes such as Reed-Solomon (RS) codes have been increasingly deployed in many distributed storage systems to reduce the large storage overhead incurred by the widely adopted replication scheme. However, these codes require significantly high resources with respect to network bandwidth and disk I/O during recovery of missing or unavailable data. It is referred as the recovery problem. In this paper, we dedicate to integrating exact minimum bandwidth regenerating codes into practical systems to solve the recovery problem. We design an implementation friendly storage code with the recently proposed BASIC framework and ZigZag decodable code for saving recovery bandwidth and disk I/O. We build a system called STORE based on this code and evaluate our prototype atop a HDFS cluster testbed with 21 nodes. As shown in this paper, the recovery bandwidth achieves minimum approximately during recovery of both data block and parity block with STORE. Another attractive result is that the recovery disk I/O also achieves minimum approximately during recovery of data block. Due to the reduction of recovery bandwidth and disk I/O, the degraded read throughput is boosted notably.
Keywords :
BASIC; Reed-Solomon codes; data handling; input-output programs; storage management; BASIC framework; HDFS cluster testbed; RS codes; Reed-Solomon codes; STORE; ZigZag decodable code; approximate minimum network bandwidth; data block; data recovery; distributed storage systems; erasure codes; exact minimum bandwidth regenerating codes; parity block; recovery disk I/O; Bandwidth; Decoding; Encoding; Maintenance engineering; Strips; Throughput; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
Type :
conf
DOI :
10.1109/BigData.2014.7004381
Filename :
7004381
Link To Document :
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