Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Proceedings of the third international conference on Genetic algorithms
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Journal of Parallel and Distributed Computing
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
Measuring the Robustness of a Resource Allocation
IEEE Transactions on Parallel and Distributed Systems
Robust Resource Allocation for Sensor-Actuator Distributed Computing Systems
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
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IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
Mapping subtasks with multiple versions on an ad hoc grid
Parallel Computing - Heterogeneous computing
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Journal of Parallel and Distributed Computing - Special issue: Algorithms for wireless and ad-hoc networks
Journal of Parallel and Distributed Computing
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This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [2, 23]. Resource allocation decisions in heterogeneous parallel and distributed computer systems and associated performance prediction are often based on estimated values of application and system parameters, whose actual values are uncertain and may be differ from the estimates. We have designed a model for deriving the degree of robustness of a resource allocation—the maximum amount of collective uncertainty in parameters within which a user-specified level of system performance can be guaranteed. The model will be presented, and we will demonstrate its ability to select the most robust resource allocation from among those that otherwise perform similarly (based on the primary performance criterion). We will show how the model can be used in off-line allocation heuristics to maximize the robustness of makespan against inaccuracies in estimates of application execution times in a cluster.