A Reinforcement Learning-based DAG Tasks Scheduling in Edge-Cloud Collaboration Systems

With the continuous development of mobile communication networks and artificial intelligence technology, the number of smart mobile devices has shown an exponential growth trend, and artificial intelligence (AI) jobs have developed unprecedentedly. However, it is difficult for resource-constrained mobile devices to meet the computational demands of these jobs. How to make full use of the dynamic resources in the wireless network to achieve efficient execution of AI jobs is the evolution direction of the next-generation network. To achieve this goal, we model the job as a directed acyclic graph (DAG), partition it into executors based on the type of task, and minimize the execution time of all jobs in 6G wireless networks by optimizing executors deployment. Considering the dynamic features of channel states and DAG topology, the optimization problem is addressed by deep reinforcement learning, i.e., Deep Q-Network (DQN). In the simulation, we manifest the performance of the DQN-based DAG task scheduling in terms of convergence and latency.