Genetic-algorithm-based real-time task scheduling with multiple goals

Authors:
Jaewon Oh;Chisu Wu
Affiliations:
SE Lab, School of Computer Science and Engineering, Seoul National University, Seoul 151-742, South Korea;SE Lab, School of Computer Science and Engineering, Seoul National University, Seoul 151-742, South Korea
Venue:
Journal of Systems and Software - Special issue: Computer systems
Year:
2004

Citing 19
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Abstract

This paper presents and evaluates a new method for real-time task scheduling in multiprocessor systems. Its objectives are to minimize the number of processors required and the total tardiness of tasks. The minimization is carried out through a multiobjective genetic algorithm (GA), because the problem has non-commensurable and competing objectives to be optimized. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific GA, the performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs. Also shown is the impact of the sparsity of a task graph on the performance of our algorithm.