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引用次数: 0

摘要

推拉通信是在资源内核操作系统之上实现的实时中间件服务。它是一种多对多通信模型,可以支持多参与者实时应用程序。它涵盖了“推”(发布者/订阅者模型)和“拉”(由接收方发起的数据传输)通信。与发布者/订阅者模型不同,不同的发布者和订阅者可以以不同的数据速率运行,还可以选择另一个(中间)节点作为它们的代理,并以所需的频率交付数据。我们特别讨论了推拉模型的端到端可预测性。操作系统中的调度机制、中间件体系结构和底层网络QoS支持都会影响数据的时效性。我们通过使用提供CPU预留的资源内核和在推拉端点之间使用保证带宽网络(DARWIN)来获得端到端的时效性和带宽保证。我们正式分析了在网络中选择最优代理位置的问题。我们讨论了该系统的实现,并在卡内基梅隆大学的集成RT-Mach-Darwin测试台上进行了详细的性能评估。我们的研究结果为使用推拉服务的应用程序的计算和通信资源调度开辟了有趣的研究方向。推拉框架可以很容易地合并到RT-CORBA事件服务模型中。
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End-to-end predictability in real-time push-pull communications
Push-pull communications is a real-time middleware service that has been implemented on top of a resource kernel operating system. It is a many-to-many communication model that can support multi-participant real-time applications. It covers both "push" (publisher/subscriber model) and "pull" (data transfer initiated by a receiver) communications. Unlike the publisher/subscriber model, different publishers and subscribers can operate at different data rates and also can choose another (intermediate) node to act as their proxy and deliver data at their desired frequency. We specifically address end-to-end predictability of the push-pull model. The scheduling mechanisms in the OS, the middleware architecture and the underlying network QoS support can impact the timeliness of data. We obtain our end-to-end timeliness and bandwidth guarantees by using a resource kernel offering CPU reservations and the use of a guaranteed bandwidth network (DARWIN) between push-pull end-points. We formally analyze the problem of choosing an optimal proxy location within a network. We discuss our implementation of this system and carry out a detailed performance evaluation on an integrated RT-Mach-Darwin testbed at Carnegie Mellon. Our results open up interesting research directions for the scheduling of computation and communication resources for the applications using the push-pull service. The push-pull framework can easily be incorporated in an RT-CORBA Event Service model.
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