With the recent advances in Internet of Things and cyber-physical systems technologies, smart industrial systems support configurable processes consisting of human interactions as well as hard real-time functions. This implies that irregularly arriving interactive tasks and traditional hard real-time tasks coexist. As the characteristics of the tasks are heterogeneous, it is not an easy matter to schedule them all at once. To cope with this situation, this article presents a new task scheduling policy that uses the notion of 'virtual real-time task' and two-phase scheduling. As hard real-time tasks must keep their deadlines, we perform offline scheduling based on genetic algorithms beforehand. This determines the processor's voltage level and memory location of each task and also reserves the virtual real-time tasks for interactive tasks. When interactive tasks arrive during the execution, online scheduling is performed on the time slot of the virtual real-time tasks. As interactive workloads evolve over time, we monitor them and periodically update the offline scheduling. Experimental results show that the proposed policy reduces the energy consumption by 66.8% on average without deadline misses and also supports the waiting time of less than 3 (s) for interactive tasks.
Bibliographical noteFunding Information:
2021. This work was supported in part by the ICT R&D program of MSIP/IITP (2019-0-00074)
© 2005-2012 IEEE.
- Genetic algorithm (GA)
- Industrial system
- Interactive task
- Real-time task
- Task scheduling