Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Simultaneous Compression of Makespan and Number of Processors Using CRP
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
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The task scheduling problem for parallel and distributed systems was extensively studied in the literature. The outcome is a large set of heuristics, each of which generate an output schedule of the given application graph by preserving the task dependency constraints with the objective of minimizing the schedule length. We extend the general task scheduling model with multiple objectives of minimizing the schedule length (for task utilization) and minimizing the number of processors used (for resource utilization). These two objectives are both conflicting and complementary, which are combined into a single objective of cost minimization in our study. In this paper, the task scheduling problem for heterogeneous systems with the unified objective is formulated by a genetic search framework.