PetaShare: A reliable, efficient and transparent distributed storage management system
27 - 43
Item Usage Stats
Modern collaborative science has placed increasing burden on data management infrastructure to handle the increasingly large data archives generated. Beside functionality, reliability and availability are also key factors in delivering a data management system that can efficiently and effectively meet the challenges posed and compounded by the unbounded increase in the size of data generated by scientific applications. We have developed a reliable and efficient distributed data storage system, PetaShare, which spans multiple institutions across the state of Louisiana. At the back-end, PetaShare provides a unified name space and efficient data movement across geographically distributed storage sites. At the front-end, it provides light-weight clients the enable easy, transparent and scalable access. In PetaShare, we have designed and implemented an asynchronously replicated multi-master metadata system for enhanced reliability and availability, and an advanced buffering system for improved data transfer performance. In this paper, we present the details of our design and implementation, show performance results, and describe our experience in developing a reliable and efficient distributed data management system for data-intensive science. © 2011 - IOS Press and the authors. All rights reserved.
Distributed data storage
Data storage equipment
Distributed computer systems
Distributed database systems
Published Version (Please cite this version)10.3233/SPR-2011-0317
Showing items related by title, author, creator and subject.
Yazıcı, Mehmet Akif; Akar, Nail (IEEE, 2013)WLANs employing IEEE802.11 standards suffer from a performance anomaly that drags down the fast nodes in the WLAN due to the slow nodes. Air time fairness has been proposed to battle this anomaly. We propose a novel ...
Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types Akçay, Alp; Biller, B. (IEEE, 2014)We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates ...
Hirzel M.; Schneider S.; Gedik, B. (Association for Computing Machinery, 2017)Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in ...