A Multi-Granularity Task Scheduling Method for Heterogeneous Computing Resources

In light of the rapid advancement of technologies related to the Internet of Things (IoT), IoT service platforms have become one of the main solutions for providing intelligent and efficient services in the industrial sector. Scheduling is an effective means to match resources and guarantee quality of service (QoS). However, existing service scheduling models and methods have not fully considered the special needs of new IoT platforms. Therefore, this article summarizes the special requirements of the new IoT platform, including the heterogeneity of IoT service platform resources, complexity and diversity of tasks, as well as considering the demand for low energy consumption and low latency. Constructed a multi-granularity task scheduling model for cloud-edge collaborative environments, which takes the special needs mentioned above into account. Combined with priority experience replay and importance sampling, a task scheduling algorithm priority replay with importance-based method in actor critic (PRIME-AC) based on deep reinforcement learning (DRL) is proposed. The experimental results show that PRIME-AC has better performance in both task execution delay and load balancing than other baselines.