Network measurement involves an inherent tradeoff between accuracy and overhead; higher accuracy typically comes at the expense of greater measurement overhead (measurement frequency, number of probe packets, etc.). Capturing the "right" balance between these two desiderata - high accuracy and low overhead - is a key challenge. However, the manner in which accuracy and overhead are traded off is specific to the measurement method, rendering apples-to-apples comparisons difficult. To address this, we put forth a novel analytical framework for quantifying the accuracy-overhead tradeoff for network measurements. Our framework, inspired by the observer effect in modern physics, introduces the notion of a network observer factor, which formally captures the relation between measurement accuracy and overhead. Using our "network observer framework", measurement methods for the same task can be characterized in terms of their network observer factors, allowing for apples-to-apples comparisons. We illustrate the usefulness of our approach by showing how it can be applied to various application domains and validate its conclusions through experimental evaluation.

翻译：网络测量在准确性与开销之间存在固有的权衡；更高的准确性通常以更大的测量开销（测量频率、探测包数量等）为代价。在这两个期望目标——高准确性与低开销——之间取得“恰当”的平衡是一个关键挑战。然而，准确性与开销之间的权衡方式因测量方法而异，这使得进行公平的比较变得困难。为解决这一问题，我们提出了一种新颖的分析框架，用于量化网络测量的准确性-开销权衡。受现代物理学中观测者效应的启发，我们的框架引入了网络观测因子的概念，该概念形式化地刻画了测量准确性与开销之间的关系。利用我们的“网络观测框架”，针对同一任务的测量方法可以通过其网络观测因子进行表征，从而实现公平的比较。我们通过展示该方法如何应用于多个应用领域，并通过实验评估验证其结论，从而说明了我们方法的实用性。