Energy efficient task scheduling with Type-2 fuzzy uncertainty

In this paper, we have reported a new approach for employing the non dominated sorting genetic algorithm-II (NSGA-II) with the type-2 fuzzy sets in optimizing energy in real-time embedded systems. The multi-objective problem of energy efficiency and timeliness of tasks has been extensively studied. Little variations in the task timing parameters produce considerable variations in the results of the critical real-time computations. Importantly, at the system designing phase these timing parameters are completely unquantifiable. We therefore propose here a new algorithm for real-time scheduling in type-2 fuzzy uncertain domains. We have included comparative results obtained from models with crisp timing parameters and their fuzzy type-1 and type-2 counterparts. From the observations of the outcome, it is found that model with crisp timing parameters gives the worst result as energy consumption in the system is maximum at a constant earliness. The crisp model is outperformed by both fuzzy type-1 and type-2 models and ensures significant reductions in energy consumption. Whereas fuzzy type-2 model overwhelms both fuzzy type-1 and crisp model in ensuring task completions with maximum earliness. Suitable numerical examples are included to demonstrate our proposed approach.