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.

云计算平台中的高效资源利用与完美用户体验通常相互冲突。本文提出了一种名为 “云中可延迟作业在线调度” 的在线可延迟工作调度方法，采用深度强化学习模型学习调度策略，并利用多个辅助任务提供更好的状态表示和改进模型性能，从而在在线环境中合理安排部署计划，实现用户等待时间短、平台资源利用率高的目标。所提出的方法在公共数据集上进行了验证并展示了卓越的性能。