ENF-S: An Evolutionary-Neuro-Fuzzy Multi-Objective Task Scheduler for Heterogeneous Multi-Core Processors
Athena Abdi, Armin Salimi-Badr
Abstract
In this paper, an evolutionary-neuro-fuzzy-based task scheduling approach (ENF-S) to jointly optimize the main critical parameters of heterogeneous multi-core systems is proposed. This approach has two phases: first, the fuzzy neural network (FNN) is trained using a non-dominated sorting genetic algorithm (NSGA-II), considering the critical parameters of heterogeneous multi-core systems on a training data set consisting of different application graphs. These critical parameters are execution time, temperature, failure rate, and power consumption. The output of the trained FNN determines the <i>criticality degree</i> for various processing cores based on the system's current state. Next, the trained FNN is employed as an online scheduler to jointly optimize the critical objectives of multi-core systems at runtime. Due to the uncertainty in sensor measurements and the difference between computational models and reality, applying the fuzzy neural network is advantageous. The efficiency of ENF-S is investigated in various aspects including its joint optimization capability, appropriateness of generated fuzzy rules, comparison with related research, and its overhead analysis through several experiments on real-world and synthetic application graphs. Based on these experiments, our ENF-S outperforms the related studies in optimizing all design criteria. Its improvements over related methods are estimated <inline-formula><tex-math notation="LaTeX">${19.21\%}$</tex-math></inline-formula> in execution time, <inline-formula><tex-math notation="LaTeX">${13.07\%}$</tex-math></inline-formula> in temperature, <inline-formula><tex-math notation="LaTeX">${25.09\%}$</tex-math></inline-formula> in failure rate, and <inline-formula><tex-math notation="LaTeX">${13.16\%}$</tex-math></inline-formula> in power consumption, averagely.