  Efficient resource utilization and perfect user experience usually conflict
with each other in cloud computing platforms. Great efforts have been invested
in increasing resource utilization but trying not to affect users' experience
for cloud computing platforms. In order to better utilize the remaining pieces
of computing resources spread over the whole platform, deferrable jobs are
provided with a discounted price to users. For this type of deferrable jobs,
users are allowed to submit jobs that will run for a specific uninterrupted
duration in a flexible range of time in the future with a great discount. With
these deferrable jobs to be scheduled under the remaining capacity after
deploying those on-demand jobs, it remains a challenge to achieve high resource
utilization and meanwhile shorten the waiting time for users as much as
possible in an online manner. In this paper, we propose an online deferrable
job scheduling method called \textit{Online Scheduling for DEferrable jobs in
Cloud} (\OSDEC{}), where a deep reinforcement learning model is adopted to
learn the scheduling policy, and several auxiliary tasks are utilized to
provide better state representations and improve the performance of the model.
With the integrated reinforcement learning framework, the proposed method can
well plan the deployment schedule and achieve a short waiting time for users
while maintaining a high resource utilization for the platform. The proposed
method is validated on a public dataset and shows superior performance.
