Two-Phase Task Scheduling in Data Relay Satellite Systems

In data relay satellite (DRS) systems, one of the most important issues is the task scheduling. Most of the existing algorithms focused on the scenario of fixed task sets; however, the scheduled tasks can be constantly changing due to the various uncertain factors in the space environment. To deal with this challenge, we propose a two-phase task scheduling algorithm to enhance the performance of scheduling, including an initial scheduling phase and a dynamic scheduling phase. In the initial scheduling phase, we construct a scheduling model with multiple constraint conditions, and also design an improved genetic algorithm with elite reserved strategy and a crowding function to find the initial scheduling solution. While in the dynamic scheduling phase, we further investigate the possibility of task preemptive switching and decomposition, by constructing a dynamic scheduling model with multiple objectives, including maximizing the total weight of scheduled tasks, minimizing the change of the scheduling scheme, and minimizing the number of decomposed subtasks. Meanwhile, a preemptive dynamic scheduling algorithm (PDSA) is designed to solve the proposed dynamic scheduling model. Our simulation results show that the proposed PDSA is superior to the existing whole rescheduling algorithm in terms of the number of completed tasks, the rescheduling rate of scheme, as well as the processing time, which can significantly improve the performance of dynamic scheduling in DRS systems.