Introduction to Algorithms
Providing Data Transfer with QoS as Agreement-Based Service
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
Scheduling deadline-constrained bulk data transfers to minimize network congestion
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Grid'5000: A Large Scale and Highly Reconfigurable Grid Experimental Testbed
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Hierarchical Replication Techniques to Ensure Checkpoint Storage Reliability in Grid Environment
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
End-host based mechanisms for implementing flow scheduling in GridNetworks
Proceedings of the first international conference on Networks for grid applications
Grid Network Dimensioning by Modeling the Deadline Constrained Bulk Data Transfers
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
Lowering Inter-datacenter Bandwidth Costs via Bulk Data Scheduling
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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In grid computing, many scientific and engineering applications require access to large amounts of distributed data. The size and number of these data collections has been growing rapidly in recent years. The costs of data transmission take a significant part of the global execution time. When communication streams flow concurrently on shared links, transport control protocols have issues allocating fair bandwidth to all the streams, and the network becomes sub-optimally used. One way to deal with this situation is to schedule the communications in a way that will induce an optimal use of the network. We focus on the case of large data transfers that can be completely described at the initialization time. In this case, a plan of data migration can be computed at initialization time, and then executed. However, this computation phase must take a small time when compared to the actual execution of the plan. We propose a best effort solution, to compute approximately, based on the uniform random sampling of possible schedules, a communication plan. We show the effectiveness of this approach both theoretically and by simulations.