Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Scheduling Distributed Applications: the SimGrid Simulation Framework
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Distributed Job Scheduling on Computational Grids Using Multiple Simultaneous Requests
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Condor-G: A Computation Management Agent for Multi-Institutional Grids
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
QoS guided min-min heuristic for grid task scheduling
Journal of Computer Science and Technology - Grid computing
Benefits of Global Grid Computing for Job Scheduling
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Job Superscheduler Architecture and Performance in Computational Grid Environments
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Using Markov chain analysis to study dynamic behaviour in large-scale grid systems
AusGrid '09 Proceedings of the Seventh Australasian Symposium on Grid Computing and e-Research - Volume 99
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We investigate effects of spoofing attacks on the scheduling and execution of basic application workflows in a moderately loaded grid computing system using a simulation model based on standard specifications. We conduct experiments to first subject this grid to spoofing attacks that reduce resource availability and increase relative load. A reasonable change in client behavior is then introduced to counter the attack, which unexpectedly causes global performance degradation. To understand the resulting global behavior, we adapt multidimensional analyses as a measurement approach for analysis of complex information systems. We use this approach to show that the surprising performance fall-off occurs because the change in client behavior causes a rearrangement of the global job execution schedule in which completion times inadvertently increase. Finally, we argue that viewing distributed resource allocation as a self-organizing process improves understanding of behavior in distributed systems such as computing grids.