A Hybrid Genetic based Approach for Real-time Reconfigurable Scheduling of OS Tasks in Uniprocessor Embedded Systems

This paper deals with the problem of scheduling uniprocessor real-time tasks by a hybrid genetic based scheduling algorithm. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at runtime. We propose a hybrid genetic based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied on an embedded system. Indeed, if the system is unfeasible, the proposed approach operates directly in a highly dynamic and unpredictable environment and improves a rescheduling performance. This proposed approach which is based on a genetic algorithm (GA) combined with a tabu search (TS) algorithm is implemented which can find an optimized scheduling strategy to reschedule the embedded system after any system disturbance was happened. We mean by a system disturbance any automatic reconfiguration which is assumed to be applied at run-time: Addition-Removal of tasks or just modifications of their temporal parameters: WCET and/or deadlines. An example used as a benchmark is given, and the experimental results demonstrate the effectiveness of proposed genetic based scheduling approach over others such as a classical genetic algorithm approach.