Entropy-based scheduling performance in real-time multiprocessor systems​

In this senior research project, we present the performance analysis of the entropy-based scheduling approach in real-time multiprocessor systems. We analyze the effect of using the entropy-based scheduling layer in deadline-based (global EDF), laxity-based (LLF), and PFair-based (PD2) scheduling algorithms by measuring the number of preemptions, the number of job migrations, and the number of task migrations. The performance comparison results between the selected scheduling algorithms with their entropy-based versions showed that the entropy layer reduces the number of task migrations for all studied algorithms and reduces the number of job migrations for LLF and PD2.