Abstract
Reducing job migrations is essential for any global multiprocessor scheduling algorithm. In this letter, we present a global, dynamic-priority, laxity-based algorithm that reduces the number of migrations on multiprocessor embedded systems by leveraging information theory principles. A simplification of the proposed scheduling theory is presented to reduce the overhead caused by using information theory. Our results show that the proposed algorithm is able to reduce the number of migrations by up to 41.21% when compared with other global, dynamic-priority, laxity-based algorithms. As the utilization per task set and the number of processors increase, simplified information-theoretic scheduling algorithm is able to improve its performance in terms of the number of migrations.
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