SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
Dynamic, Reliability-Driven Scheduling of Parallel Real-Time Jobs in Heterogeneous Systems
ICPP '02 Proceedings of the 2001 International Conference on Parallel Processing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Performance Prediction and Its Use in Parallel and Distributed Computing Systems
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Optimizing Static Job Scheduling in a Network of Heterogeneous Computers
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Performance Prediction in Production Environments
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
Dynamic, capability-driven scheduling of DAG-based real-time jobs in heterogeneous clusters
International Journal of High Performance Computing and Networking
Grid load balancing using intelligent agents
Future Generation Computer Systems
Information Sciences: an International Journal
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Coordinated rescheduling of Bag-of-Tasks for executions on multiple resource providers
Concurrency and Computation: Practice & Experience
Stochastic DAG scheduling using a Monte Carlo approach
Journal of Parallel and Distributed Computing
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This paper investigates the underlying impact of predictive inaccuracies on execution scheduling, with particular reference to execution time predictions. This study is conducted from two perspectives: from that of job selection and from that of resource allocation, both of which are fundamental components in execution scheduling. A new performance metric, termed the degree of misperception, is introduced to express the probability that the predicted execution times of jobs display different ordering characteristics from their real execution times due to inaccurate prediction. Specific formulae are developed to calculate the degree of misperception in both job selection and resource allocation scenarios. The parameters which influence the degree of misperception are also extensively investigated. The results presented in this paper are of significant benefit to scheduling approaches that take into account predictive data; the results are also of importance to the application of these scheduling techniques to real-world high-performance systems.