Future Generation Computer Systems - Special issue on metacomputing
Inference and Labeling of Metric-Induced Network Topologies
Inference and Labeling of Metric-Induced Network Topologies
Experiences in traceroute and available bandwidth change analysis
Proceedings of the ACM SIGCOMM workshop on Network troubleshooting: research, theory and operations practice meet malfunctioning reality
Network radar: tomography from round trip time measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
The Globus Striped GridFTP Framework and Server
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Optimizing BGP security by exploiting path stability
Proceedings of the 13th ACM conference on Computer and communications security
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Nowadays grids connect up to thousands communicating resources that may interact in a partially or totally coordinated way. Consequently, applications running upon this kind of platform often involve massively concurrent bulk data transfers. In order to optimize overall completion times, those transfers have to be scheduled based on knowledge about network performances and topology. Identifying and inferring performances of a network topology is a classic problem. Achieving this by using only end-to-end measurements at the application level is a method known as network tomography. When topology reflects capacities of sets of links with respect to a metric, the model used to represent the topology obtained is called a Metric-Induced Network Topology (MINT). Such a type of representation, obtained using statistical methods, has been widely used in order to represent performances of client/server communication protocols. However, it is no longer accurate when dealing with grids. In this paper, we present a novel representation of the infered knowledge from multiple source and multiple destination measurements.