Stochastic Bandwidth Estimation in Networks With Random Service
Stochastic Bandwidth Estimation in Networks With Random Service
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DOI：
10.1109/tnet.2013.2261914
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
Ralf Lübben;M. Fidler;J. Liebeherr
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文献类型：
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作者：
Ralf Lübben;M. Fidler;J. Liebeherr
有线网络的可用带宽估计方法有很多，其有效性也有很多文献记载。然而，大多数方法无法可靠地预测无线环境中的带宽可用性。无线信道条件的可变性增加使得带宽估计更加困难，这是公认的。然而，一个（令人满意的）解释，为什么这些方法是失败的失踪。本文旨在提供洞察无线网络中的带宽估计问题，或更广泛地说，在网络中的随机服务。我们表示带宽的可用性方面的边界函数与一个定义的违规概率。利用随机最小加线性系统理论的性质，带宽估计的任务被制定为推断一个未知的边界函数从测量的探测流量。我们目前的推导表明，简单地使用随机服务的网络中的可用带宽的期望值会导致系统高估的流量偏离。此外，我们表明，在多跳设置与随机服务在每个节点上，可用带宽估计需要观察（原则上无限）长的时间段。我们提出了一种新的估计方法，随机服务的基础上，利用统计方法的迭代恒定速率探测。我们展示了如何实现我们的估计方法，以达到良好的精度和置信水平。我们评估我们的有线单跳和多跳网络，以及无线网络的方法。
Numerous methods for available bandwidth estimation have been developed for wireline networks, and their effectiveness is well-documented. However, most methods fail to predict bandwidth availability reliably in a wireless setting. It is accepted that the increased variability of wireless channel conditions makes bandwidth estimation more difficult. However, a (satisfactory) explanation why these methods are failing is missing. This paper seeks to provide insights into the problem of bandwidth estimation in wireless networks or, more broadly, in networks with random service. We express bandwidth availability in terms of bounding functions with a defined violation probability. Exploiting properties of a stochastic min-plus linear system theory, the task of bandwidth estimation is formulated as inferring an unknown bounding function from measurements of probing traffic. We present derivations showing that simply using the expected value of the available bandwidth in networks with random service leads to a systematic overestimation of the traffic departures. Furthermore, we show that in a multihop setting with random service at each node, available bandwidth estimates requires observations over (in principle infinitely) long time periods. We propose a new estimation method for random service that is based on iterative constant-rate probes that take advantage of statistical methods. We show how our estimation method can be realized to achieve both good accuracy and confidence levels. We evaluate our method for wired single-and multihop networks, as well as for wireless networks.