With more businesses are running online, the scale of data centers is increasing dramatically. The task-scheduling operation with traditional heuristic algorithms is facing the challenges of uncertainty and complexity of the data center environment. It is urgent to use new technology to optimize the task scheduling to ensure the efficient task execution. This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization. The proposed scheduling model was trained, tested, and compared with classical scheduling algorithms on real data center datasets in experiments to show the effectiveness and efficiency. The experiment report showed that the proposed algorithm worked better than the compared classical algorithms in the key performance metrics: average delay time of tasks, task distribution in different delay time levels, and task congestion degree.

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Airport is an ideal application area of simulation. Passenger flow is very important for the airport waiting room management and operation. A simulation model is developed to analyse the airport waiting room using commercially available simulation software Arena 9.01 in this paper. The simulation model can be used to gain insights into the presence of bottlenecks and their causes, and to evaluate the key performance measures of the system. Through optimization, different resources can be reassigned. Therefore, it provides the basis for increasing utilization of facilities and improving service level.

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