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Applications of machine learning approach on multi-queue message scheduling

机译：机器学习方法在多队列消息调度中的应用

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Due to limited resource contentions and deadline constraints, messages on the controller area network (CAN) are competing for service from the common resources. This problem can be resolved by assigning priorities to different message classes to satisfy time-critical applications. Actually, because of the fluctuation of network traffic or an inefficient use of resources, these static or dynamic priority policies may not guarantee flexibility for different kinds of messages in real-time scheduling. Consequently, the message transmission which cannot comply with the timing requirements or deadlines may deteriorate system performance significantly. In this paper, we have proposed a controller-plant model, where the plant is analogous to a message queue pool (MQP) and the message scheduling controller (MSC) is responsible to dispatch resources for queued messages according to the feedback information from the MQP. The message scheduling controller, which is realized by the radial basis function (RBF) network, is designed with machine learning algorithm to compensate the variations in plant dynamics. The MSC with the novel hybrid learning schemes can ensure a low and stable message waiting time variance (or a uniform distribution of waiting time) and lower transmission failures. A significant emphasis of the MSC is the variable structure of the RBF model to accommodate to complex scheduling situations. Simulation experiments have shown that several variants of the MSC significantly improve overall system performance over the static scheduling strategies and the dynamic earliest-deadline first (EDF) algorithms under a wide range of workload characteristics and execution environments.
机译：由于有限的资源争用和截止期限限制，控制器局域网（CAN）上的消息正在争夺来自公共资源的服务。通过为不同的消息类别分配优先级来满足时间紧迫的应用程序，可以解决此问题。实际上，由于网络流量的波动或资源使用效率低下，这些静态或动态优先级策略可能无法保证实时调度中各种消息的灵活性。因此，不符合时序要求或期限的消息传输可能会严重降低系统性能。在本文中，我们提出了一个控制器工厂模型，其中工厂类似于消息队列池（MQP），消息调度控制器（MSC）负责根据来自MQP的反馈信息为排队的消息分配资源。 。由径向基函数（RBF）网络实现的消息调度控制器采用机器学习算法进行设计，以补偿植物动态变化。具有新颖的混合学习方案的MSC可以确保较低且稳定的消息等待时间方差（或等待时间的均匀分布）和较低的传输失败率。 MSC的一个重要重点是RBF模型的可变结构，以适应复杂的调度情况。仿真实验表明，在各种工作负载特征和执行环境下，MSC的几种变体比静态调度策略和动态最早截止时间优先（EDF）算法显着提高了整体系统性能。

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