An Energy-Efficient Task Scheduling Method for CPU-GPU Heterogeneous Cloud

Given the problems of high energy consumption and unreasonable resource utilization in current heterogeneous cloud computing platforms, this paper proposes an energy-efficient task scheduling strategy. First, by analyzing the relationship between the energy consumption of physical machines and the utilization rate of CPU-GPU resources, we can establish the task scheduling model and energy consumption model. Second, a task scheduling method HCGTS (Heterogeneous CPU-GPU Task Scheduling) for energy consumption optimization of CPU-GPU heterogeneous cloud platform is proposed. Different from traditional subtasks, we divide the multi-task data flows into CPU tasks, GPU tasks and pending tasks, which are scheduled and executed by different processors to implement multi-task parallel scheduling. In addition, the execution order of subtasks is dynamically adjusted to realize the load balance of CPU and GPU, thereby reducing the task completion time and overall energy consumption. Finally, the simulation experiment is completed on the cloud simulation software. Through comparing with the other three algorithms, the HCGTS algorithm has better performance in terms of execution time, resource utilization and energy consumption optimization.