Performance evaluation of a distributed storage service in community network clouds. (20th September 2015)

• Record Type: Journal Article
• Authors: Selimi, Mennan; Freitag, Felix; Cerdà‐Alabern, Llorenç; Veiga, Luís
• Other Names: Frincu Mark guestEditor.; Bósa Károly guestEditor.; Rong Chunming guestEditor.; Liu Lu guestEditor.; Chen Guolong guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 11(2016)
• Page Start: 3131
• Page End: 3148
• Publication Date: 2015-09-20
• Subjects: community networks -- community cloud -- cloud storage; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3658
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Community networks are self‐organized and decentralized communication networks built and operated by citizens, for citizens. The consolidation of today's cloud technologies offers now, for community networks, the possibility to collectively develop community clouds, building upon user‐provided networks and extending toward cloud services. Cloud storage, and in particular secure and reliable cloud storage, could become a key community cloud service to enable end‐user applications. In this paper, we evaluate in a real deployment the performance of Tahoe least‐authority file system (Tahoe‐LAFS), a decentralized storage system with provider‐independent security that guarantees privacy to the users. We evaluate how the Tahoe‐LAFS storage system performs when it is deployed over distributed community cloud nodes in a real community network such as Guifi.net. Furthermore, we evaluate Tahoe‐LAFS in the Microsoft Azure commercial cloud platform, to compare and understand the impact of homogeneous network and hardware resources on the performance of the Tahoe‐LAFS. We observed that the write operation of Tahoe‐LAFS resulted in similar performance when using either the community network cloud or the commercial cloud. However, the read operation achieved better performance in the Azure cloud, where the reading from multiple nodes of Tahoe‐LAFS benefited from the homogeneity of the network and nodes. Our results suggest that Tahoe‐LAFS can run on community network clouds with suitable performance for the needed end‐user experience. Copyright © 2015 John Wiley & Sons, Ltd.