Communications of the ACM
Experiences with resource provisioning for scientific workflows using Corral
Scientific Programming
Retelab: A geospatial grid web laboratory for the oceanographic research community
Future Generation Computer Systems
Differentiated replication strategy in data centers
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
The data access layer in the GRelC system architecture
Future Generation Computer Systems
Implementation of a distributed file storage with replica management in Peer-to-Peer environments
International Journal of Ad Hoc and Ubiquitous Computing
A Fast Location Service for Partial Spatial Replicas
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
Enabling large-scale scientific workflows on petascale resources using MPI master/worker
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid
Journal of Parallel and Distributed Computing
A classification of file placement and replication methods on grids
Future Generation Computer Systems
Hi-index | 0.02 |
Distributed computing systems employ replication to improve overall system robustness, scalability, and performance. A Replica Location Service (RLS) offers a mechanism to maintain and provide information about physical locations of replicas. This paper defines a design framework for RLSs that supports a variety of deployment options. We describe the RLS implementation that is distributed with the Globus Toolkit and is in production use in several Grid deployments. Features of our modular implementation include the use of soft-state protocols to populate a distributed index and Bloom filter compression to reduce overheads for distribution of index information. Our performance evaluation demonstrates that the RLS implementation scales well for individual servers with millions of entries and up to 100 clients. We describe the characteristics of existing RLS deployments and discuss how RLS has been integrated with higher-level data management services.