Network performance tomography infers performance metrics on internal network links with end-to-end measurements. Existing results in this domain are mainly Boolean-based, i.e., they check whether or not a link is identifiable, and return the exact value on identifiable links. If a link is not identifiable, the Boolean-based solution gives no performance result for the link. In this paper, we extend Boolean-based network tomography to bound-based network tomography where the lower and upper bounds are derived for unidentifiable links. We develop an efficient algorithm to obtain the tightest total error bound, and present a solution that can significantly reduce the total number of measurement paths required for deriving the tightest total error bound. Furthermore, we propose a method to deploy a new monitor such that the total error bound could be maximally reduced. Compared to the random monitor deployment and the monitor deployment that maximizes the total number of identifiable links, our monitor deployment method can lead to up to 15 and 2.4 times more reduction on total error bound, respectively.

中文翻译：

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具有附加度量的网络性能层析成像中的界推断

网络性能层析成像可以通过端到端测量来推断内部网络链路上的性能指标。该域中的现有结果主要基于布尔值，即，它们检查链接是否可识别，并在可识别的链接上返回准确的值。如果无法识别链接，则基于布尔的解决方案将不给出链接的性能结果。在本文中，我们将基于布尔的网络断层摄影技术扩展到基于边界的网络断层摄影技术，在该方法中，上下边界是针对无法识别的链接而得出的。我们开发了一种有效的算法来获取最严格的总误差范围，并提出了一种解决方案，该解决方案可以显着减少得出最严格的总误差范围所需的测量路径总数。此外，我们提出了一种部署新监视器的方法，以便可以最大程度地减少总错误范围。与随机监视器部署和最大化可识别链接总数的监视器部署相比，我们的监视器部署方法可以分别使错误总数减少多达15倍和2.4倍。