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 thecriticality degreefor 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${19.21\%}$in execution time,${13.07\%}$in temperature,${25.09\%}$in failure rate, and${13.16\%}$in power consumption, averagely.

We investigate multi-criteria optimization and Pareto front generation. Given an application modeled as a Directed Acyclic Graph (DAG) of tasks and a multicore architecture, we produce a set of non-dominated (in the Pareto sense) static schedules of this DAG onto this multicore. The criteria we address are the execution time, reliability, power consumption, and peak temperature. These criteria exhibit complex antagonistic relations, which make the problem challenging. For instance, improving the reliability requires adding some redundancy in the schedule, which penalizes the execution time. To produce Pareto fronts in this 4-dimension space, we transform three of the four criteria into constraints (the reliability, the power consumption, and the peak temperature), and we minimize the fourth one (the execution time of the schedule) under these three constraints. By varying the thresholds used for the three constraints, we are able to produce a Pareto front of non-dominated solutions. We propose two algorithms to compute static schedules. The first is a ready list scheduling heuristic called Execution time, Reliability, POwer consumption and Temperature (ERPOT). ERPOT actively replicates the tasks to increase the reliability, uses Dynamic Voltage and Frequency Scaling to decrease the power consumption, and inserts cooling times to control the peak temperature. The second algorithm uses an Integer Linear Programming (ILP) program to compute an optimal schedule. However, because our multi-criteria scheduling problem is NP-complete, the ILP algorithm is limited to very small problem instances. Comparisons showed that the schedules produced by ERPOT are on average only 10 percent worse than the optimal schedules computed by the ILP program, and that ERPOT outperforms the PowerPerf-PET heuristic from the literature on average by 33 percent.