摘要
Real-time task scheduling is of primary significance in multiprocessor systems.Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems.In this paper,we represent those two goals as the minimization of the average response time and the average task laxity.To achieve this,we propose a genetic-based algorithm with problem-specific and efficient genetic operators.Adaptive control parameters are also employed in our work to improve the genetic algorithms' efficiency.The simulation results show that our proposed algorithm outperforms its counterpart considerably by up to 36% and 35% in terms of the average response time and the average task laxity,respectively.
Real-time task scheduling is of primary significance in multiprocessor systems. Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems. In this paper, we represent those two goals as the minimization of the average response time and the average task laxity. To achieve this, we propose a genetic-based algorithm with problem-specific and efficient genetic operators. Adaptive control parameters are also employed in our work to improve the genetic algorithms＇ efficiency. The simulation results show that our proposed algorithm outperforms its counterpart considerably by up to 36% and 35% in terms of the average response time and the average task laxity, respectively.
