论文标题
具有固定和不同数据包尺寸的令牌桶过滤器的马尔可夫性能模型
Markovian Performance Model for Token Bucket Filter with Fixed and Varying Packet Sizes
论文作者
论文摘要
我们考虑一种具有异质流量的象征性水桶机制，重点是积压，延迟和数据包丢失属性。以前的型号考虑了固定尺寸数据包的情况，即带有排队行为的“每个数据包”和m/d/1视图。我们将异质流量分为几个数据包大小类，并具有单个泊松到达强度。随附的排队模型是一个“完整状态”模型，即缓冲区内容不会减少到单个数量，而是包含数据包大小类别的详细内容。这产生了高模型的高基数，为其提供了上限。分析结果包括特定于类的积压，延迟和损失统计，并伴随着离散事件模拟的结果。
We consider a token bucket mechanism serving a heterogeneous flow with a focus on backlog, delay and packet loss properties. Previous models have considered the case for fixed size packets, i.e. "one token per packet" with and M/D/1 view on queuing behavior. We partition the heterogeneous flow into several packet size classes with individual Poisson arrival intensities. The accompanying queuing model is a "full state" model, i.e. buffer content is not reduced to a single quantity but encompasses the detailed content in terms of packet size classes. This yields a high model cardinality for which upper bounds are provided. Analytical results include class specific backlog, delay and loss statistics and are accompanied by results from discrete event simulation.
