  Various resources as the essential elements of data centers, and the
completion time is vital to users. In terms of the persistence, the periodicity
and the spatial-temporal dependence of stream workload, a new Storm scheduler
with Advantage Actor-Critic is proposed to improve resource utilization for
minimizing the completion time. A new weighted embedding with a Graph Neural
Network is designed to depend on the features of a job comprehensively, which
includes the dependence, the types and the positions of tasks in a job. An
improved Advantage Actor-Critic integrating task chosen and executor assignment
is proposed to schedule tasks to executors in order to better resource
utilization. Then the status of tasks and executors are updated for the next
scheduling. Compared to existing methods, experimental results show that the
proposed Storm scheduler improves resource utilization. The completion time is
reduced by almost 17\% on the TPC-H data set and reduced by almost 25\% on the
Alibaba data set.
