DocumentCode
659507
Title
Distributed storage evaluation on a three-wide inter-data center deployment
Author
Yih-Farn Chen ; Daniels, Stephen ; Hadjieleftheriou, MariosMarios ; Pingkai Liu ; Chao Tian ; Vaishampayan, Vijay
Author_Institution
Shannon Lab., AT&T Labs.-Res., Florham Park, NJ, USA
fYear
2013
fDate
6-9 Oct. 2013
Firstpage
17
Lastpage
22
Abstract
The demand for cloud storage is exploding as an ever increasing number of enterprises and consumers are storing and processing their data in the cloud. Hence, distributed object storage solutions (e.g., QFS, Swift, HDFS) are becoming very critical components of any cloud infrastructure. These systems are able to offer good reliability by distributing redundant information across a large number of commodity servers, making it possible to achieve 10 nines and beyond with relative ease. One drawback of these systems is that they are usually designed for deployment within a single data center, where node-to-node latencies are small. Geo-replication (i.e., distributing redundant information across data centers) for most open-source storage systems is, to the best of our knowledge, accomplished by asynchronously mirroring a given deployment. Given that geo-replication is critical for ensuring very high degrees of reliability (e.g., for achieving 16 nines), in this work we evaluate how these storage systems perform when they are directly deployed in a WAN setting. To this end, three popular distributed object stores, namely Quantcast-QFS, Swift and Tahoe-LAFS, are considered and tested in a three-wide data center environment and our findings are reported.
Keywords
cloud computing; computer centres; computer network reliability; public domain software; redundancy; wide area networks; HDFS; Quantcast-QFS; Swift; Tahoe-LAFS; WAN; cloud infrastructure; commodity servers; data processing; data storage; distributed object storage evaluation; geo-replication; node-to-node latencies; open-source storage systems; redundant information distribution; three-wide interdata center deployment; Bandwidth; Containers; Maintenance engineering; Reliability; Servers; Throughput; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data, 2013 IEEE International Conference on
Conference_Location
Silicon Valley, CA
Type
conf
DOI
10.1109/BigData.2013.6691656
Filename
6691656
Link To Document