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.

翻译：在云计算平台中，高效的资源利用与完美的用户体验通常存在冲突。为提升资源利用率同时尽可能不影响用户体验，业界已投入大量努力。为更好地利用分散在整个平台中的剩余计算资源碎片，平台以折扣价格向用户提供可延迟作业服务。对于此类可延迟作业，用户可提交在未来弹性时间范围内运行特定连续时长的任务，并享受大幅价格优惠。在部署按需作业后，这些可延迟作业需在剩余容量下进行调度，如何以在线方式实现高资源利用率，同时尽可能缩短用户等待时间，仍是一个挑战。本文提出一种名为\textit{Online Scheduling for DEferrable jobs in Cloud}（\OSDEC{}）的在线可延迟作业调度方法，该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优的状态表征以提升模型性能。通过集成强化学习框架，所提方法能够有效规划部署调度，在保持平台高资源利用率的同时实现较短的用户等待时间。基于公开数据集的验证表明，该方法具有优越性能。