Energy Efficient Task Scheduling for Real-Time Embedded Systems in a Fuzzy Uncertain Environment

During the designing phase of real-time embedded systems (RTESs), available information on task characteristics is either incomplete or imprecise. So, task timing constraints are mostly approximated estimations by designers. This indicates that there are underlying uncertainties in these timing constraints, which can be appropriately modeled using fuzzy numbers. Moreover, feasible scheduling of tasks and energy efficiency are two essential requirements for better utilization and durability of RTESs. Thus, justifiable performance of this kind of systems warrants energy savings amid timely production of the computational outputs, although these two issues are mutually contradictory. This article reports a novel formulation of the energy efficient real-time scheduling problem in a fuzzy uncertain environment and proposes a novel solution approach called “ε-constraint coupled energy efficient genetic algorithm (ε-EEGA).” The working of the proposed approach is demonstrated taking a real-life example. Also, a thorough comparative analysis is provided considering well-known existing approaches including multiobjective evolutionary algorithms. Results are compared using popular performance metrics, which suggests that the proposed ε-EEGA is efficient in giving better energy savings with faster computations than its existing counterparts. Standard statistical tests such as analysis of variance and Kruskal-Wallis are performed.