Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
On choosing a task assignment policy for a distributed server system
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
A Slowdown Model for Applications Executing on Time-Shared Clusters of Workstations
IEEE Transactions on Parallel and Distributed Systems
Online Prediction of the Running Time of Tasks
Cluster Computing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A High-Performance Mapping Algorithm for Heterogeneous Computing Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Segmented Min-Min: A Static Mapping Algorithm for Meta-Tasks on Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Why the Mean is Inadequate for Accurate Scheduling Decisions
ISPAN '99 Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Journal of Parallel and Distributed Computing
Stochastic robustness metric and its use for static resource allocations
Journal of Parallel and Distributed Computing
International Journal of Distance Education Technologies
Heterogeneous makespan and energy-constrained DAG scheduling
Proceedings of the 2013 workshop on Energy efficient high performance parallel and distributed computing
Stochastic DAG scheduling using a Monte Carlo approach
Journal of Parallel and Distributed Computing
Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster
Future Generation Computer Systems
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In this study, we address the meta-task scheduling problem in heterogeneous computing (HC) systems, which is to find a task assignment that minimizes the schedule length of a meta-task composed of several independent tasks with no data dependencies. The fact that the meta-task scheduling problem in HC systems is NP-hard has motivated the development of many heuristic scheduling algorithms. These heuristic algorithms, however, neglect the stochastic nature of task execution times in an attempt to minimize a deterministic objective function, which is the maximum of the expected values of machine loads. Contrary to existing heuristics, we account for this stochastic nature by modeling task execution times as random variables. We, then, formulate a stochastic scheduling problem where the objective is to minimize the expected value of the maximum of machine loads. We prove that this new objective is underestimated by the deterministic objective function and that an optimal task assignment obtained with respect to the deterministic objective function could be inefficient in a real computing platform. In order to solve the stochastic scheduling problem posed, we develop a genetic algorithm based scheduling heuristic. Our extensive simulation studies show that the proposed genetic algorithm can produce better task assignments as compared to existing heuristics. Specifically, we observe a performance improvement on the relative cost heuristic (M.-Y. Wu and W. Shu, A high-performance mapping algorithm for heterogeneous computing systems, in: Int. Parallel and Distributed Processing Symposium, San Francisco, CA, April 2001) by up to 61%.