Accurate and Efficient Network Tomography Through Network Coding

Abstract

Abstract-Accurate and efficient measurement of networkinternal characteristics is critical for the management and maintenance of large-scale networks. In this paper, we propose a linear algebraic network tomography (LANT) framework for the active inference of link loss rates on mesh topologies through network coding. Probe packets are transmitted from the sources to the destinations along a set of paths. Intermediate nodes linearly combine the received probes and transmit the coded probes using predetermined coding coefficients. Although a smaller probe size can reduce the bandwidth usage of the network, the inference framework is not valid if the probe size falls below a certain threshold. To this end, we determine the minimum probe packet size, which is necessary and sufficient to establish the mapping between the contents of the received probes and the losses on the different sets of paths. Then, we develop algorithms to find the coding coefficients such that the minimum probe size is achieved. We propose a linear algebraic approach to develop consistent estimators of link loss rates, which converge to the actual loss rates as the number of probes increases. Simulation results show that the LANT framework achieves better estimation accuracy than the belief propagation algorithm for a large number of probe packets.

A Review of Advanced Algebraic Approaches Enabling Network Tomography for Future Network Infrastructures

“…The work in [35] extends [34] by addressing the problems that were not explored there. Firstly, an algorithm is developed in order to find a valid probe coding scheme such that the minimum probe size is achieved.…”

“…Firstly, an algorithm is developed in order to find a valid probe coding scheme such that the minimum probe size is achieved. To that end, the authors of [35] construct auxiliary tree topologies, T e , each associated with one particular end link e = (h, r) to the root node, where the root node of T e corresponds to the destination node r in G. The leaves of the auxiliary tree correspond to the sources of G that use link e to relay packets to the root node. The number of leaves is equal to the number of paths that traverse link e. Each T e corresponds to the subgraph G e .…”

Network tomography application in mobile ad-hoc network using stitching algorithm

“…In addition to topology, the NT approach as described in [12]- [15] and in [16]- [21] has been extensively used to evaluate aspects of network performance such as the loss rate of links and the delay distribution of a link. NT makes it possible to evaluate packet loss ratios and link delays only from end-to-end observations, without knowing the internal network structure.…”

“…NT for correlated links assumes that links are no longer statistically independent and that the performance of one link can affect the performance of others. Gui et al [21] proposed a linear algebraic networkscale tomography framework to estimate by active inference link loss rates on mesh topologies using NT. Demir et al [22] developed a searchlight localization algorithm with NT to locate the sources of distributed denial of service (DDoS) from sequences of DDoS attacks.…”

Network tomography application in mobile ad-hoc networks.

“…The network tomography for correlated links assumes the links are not statistically independent any more and the performance of one link can eect the performance of others. Gui et al [29] proposed a linear algebraic network-scale tomography framework to estimates the active inference of the link loss rates on mesh topologies using network tomography.…”