By Sergei Gorlatch, Marian Bubak, Thierry Priol
This quantity includes the edited complaints of the second one CoreGRID Integration Workshop, CGIW'2006, held October 2006 in Krakow, Poland. A "Network of Excellence" funded via the eu Commission’s 6th Framework application, CoreGRID goals to bolster and increase medical and technological excellence within the zone of Grid and Peer-to-Peer applied sciences through bringing jointly a severe mass of well-established researchers from forty-one eu study associations. Designed for a certified viewers of practitioners and researchers, the amount is additionally appropriate for advanced-level scholars in desktop technological know-how.
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Additional info for Achievements in European Research on Grid Systems: CoreGRID Integration Workshop 2006(Selected Papers)
We can use standard techniques to com′ i−1 . pute the second term of the difference. Consider that Sm−1 = m i=1 i · q ′ ′ By multiplying Sm−1 by q and taking the difference (1 − q)Sm−1 , we get −mq m ′ . 9 Upper bound of benefit relative to maximum time Upper bound of benefit relative to maximum time In Figures 1(a) and 1(b) we depict the time that we can save relative to T (E[W ]/T ), considering Equation 2. 05. From Eq. 2 we conclude that the maximum time that a checkpoint comparison can save converges to T , when m → ⊤.
The task replication works by loosely coupling the execution of the redundant instances of a same task, which are configured for reporting selective checkpoint digests. Note that workers processing instances are not aware of each other (otherwise the risk of collusion would increase). The supervisor follows the progress of the coupled instances of a task through the messages holding the checkpoint digests sent back by these instances, validating the received checkpoint digests of the selected execution point through comparison as soon as a majority of results has been received.
The eNANOS project is based on the idea of having a good low level support for performing a good high level scheduling taking into account these ideas: a fine grain control between scheduling levels, dynamic allocation (MPI+OpenMP jobs) to improve system performance, detailed information about current scheduling and performance to improve future scheduling Integration of the eNANOS Execution Framework with GRMS 27 decisions, and, based on this accurate information, efficient scheduling (in terms of slowdown) based on performance prediction.