基于深度强化学习的云作业调度及仿真研究

针对复杂瞬变的多用户多队列多数据中心云计算环境中作业调度困难的问题,提出一种基于深度强化学习的作业调度方法.建立了云作业调度系统模型及其数学模型,并建立了由传输时间、等待时间和执行时间三部分构成的优化目标.基于深度强化学习设计了作业调度算法,给出了算法的状态空间、动作空间和奖赏函数.设计与开发了云作业仿真调度器,完成作...

Job Scheduling and Simulation in Cloud Based on Deep Reinforcement Learning

To solve the difficulty in job scheduling in the complex and transient multi-user, multi-queue, and multi-data-center cloud computing environment, this paper proposed a job scheduling method based on deep reinforcement learning. A system model of cloud job scheduling and its mathematical model were built, and an optimization goal consisting of transmission time, waiting time, and execution time was obtained. A job scheduling algorithm based on deep reinforcement learning was designed, and its state space, action space, and reward function were given. A simulated cloud job scheduler was designed and developed, and simulated scheduling experiments were conducted on it. The results show that compared with benchmark algorithms such as random scheduling, round-robin scheduling, firstfit, and optimal fit, the proposed algorithm could effectively reduce the overall makespan of the jobs.