Inferring Traffic Flow Characteristics from Aggregated-flow Measurement

In the Internet, a statistical perspective of global traffic flows has been considered as an important key to network operations and management. Nonetheless, it is expensive or sometime difficult to measure statistics of each flow directly. Therefore, it is of practical importance to infer unobservable statistical characteristics of individual flows from characteristics of the aggregated-flows, which are easily observed at some links (e.g., router interfaces) in the network. In this paper, we propose a new approach to such inference problems based on finding an inverse function from (observable) probabilities of some states on aggregated-flows to (unobservable) probabilities of some states on flows on a discrete state model, and provide a method inferring arrival rate statistics of individual flows (the OD traffic matrix inference). Our method is applicable to cases not covered by the existing normal-based methods for the OD traffic matrix inference. We also show simulation results on several flow topologies, which indicate potential of our approach

Accurate OD Traffic Matrix Estimation Based on Resampling of Observed Flow Data

It is important to observe the statistical characteristics of global flows, which are defined as series of packets between networks, for the management and operation of the Internet. However, because the Internet is a diverse and large-scale system organized by multiple distributed authorities, it is not practical (sometimes impossible) to directly measure the precise statistical characteristics of global flows. In this paper, we consider the problem of estimating the traffic rate of every unobservable global flow between corresponding origin-destination (OD) pair (hereafter referred to as “individual-flows”) based on the measured data of aggregated traffic rates of individual flows (hereafter referred to as “aggregated-flows”), which can be easily measured at certain links (e.g., router interfaces) in a network. In order to solve the OD traffic matrix estimation problem, the prior method uses an inverse function mapping from the probability distributions of the traffic rate of aggregated-flows to those of individual-flows. However, because this inverse function method is executed recursively, the accuracy of estimation is heavily affected by the initial values of recursion and variation of the measurement data. In order to solve this issue and improve estimation accuracy, we propose a method based on a resampling of measurement data to obtain a set of solution candidates for OD traffic matrix estimation. The results of performance evaluations using a real traffic trace demonstrate that the proposed method achieves better estimation accuracy than the prior method.Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018 (APSIPA ASC 2018), 12-15 November 2018, Honolulu, Hawaii, US