Efficient parallel algorithm for robot inverse dynamics computation
IEEE Transactions on Systems, Man and Cybernetics
Centralized and distributed operating systems
Centralized and distributed operating systems
Introduction to parallel computing
Introduction to parallel computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Parallel and distributed computing handbook
Parallel and distributed computing handbook
IEEE Transactions on Parallel and Distributed Systems
A parallel island model genetic algorithm for the multiprocessor scheduling problem
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
A comparison of list schedules for parallel processing systems
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
Security-Driven Heuristics and A Fast Genetic Algorithm for Trusted Grid Job Scheduling
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Instruction scheduling using MAX-MIN ant system optimization
GLSVLSI '05 Proceedings of the 15th ACM Great Lakes symposium on VLSI
A performance study of multiprocessor task scheduling algorithms
The Journal of Supercomputing
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Scheduling tasks on a multi-processor system involves making a choice as to the order in which several tasks can be executed and assigned to processors. The problem is to find a schedule that will minimize the execution time of a program. Because task scheduling on a multi-processor system is known to be an NP-complete problem, many heuristics have been developed, each of which may find optimal or near optimal schedulings under different circumstances. List Scheduling, in particular, employs heuristics to choose among all tasks that are ready to be executed, the combination of tasks that should be scheduled in the next cycle. It does this by keeping a list of "ready" tasks which is prioritized based on a particular heuristic. In this paper, we present four common heuristics used by List Scheduling and compare their performance with that of our multi-heuristic based solution.The proposed solution is to use a genetic algorithm to find a combination of the four heuristics that, for a particular instance of the task scheduling problem, outperforms a scheduling based on only one of the four heuristics. We believe that, by using a mixture of the four heuristics, the size of the subset of all possible schedulings that we search increases and thus we have a higher chance of finding a better scheduling. The results of our experiments show that schedulings found with the proposed multi-heuristic list scheduling genetic algorithm outperforms those found with each one of the four list scheduling heuristics alone.