Abstract:

With the rapid development of GPU,FPGA,and other computing units,the heterogeneous computing platform is widely used in cloud computing,the data center,the Internet of things,and other fields because of its rich computing resources,flexible architecture,and strong parallel processing capability.Aiming at the task scheduling problem of heterogeneous computing resources and lack of global task information for heterogeneous computing platforms,the task execution model is carried out according to the attributes of tasks and computing resources.Then,we use graph neural networks to encode the scalable state information on tasks and computing resources,and the characteristic of tasks and computing resources are aggregated on three levels,which solves the problems of the uncertain number of tasks and lack of global information.To minimize the average task completion time,we design a task scheduling algorithm based on the Deep Deterministic Policy Gradient(DDPG).Experimental results show that compared with Random scheduling,First in First Out scheduling,Shortest Job First scheduling,Roulette scheduling,and existing reinforcement learning scheduling algorithm,the average task completion time of our algorithm(JEDERL,Job Embedding Device Embedding Reinforcement Learning)is reduced by 27.8%,12.6%,28.6%,21.9%,and 5.3%,respectively and that the proposed algorithm stays stable when the number of cluster servers and tasks changes.

Key words: heterogeneous computing, task scheduling, reinforcement learning, graph neural networks