The Inference of Link Loss Rates with Internal Monitors

Abstract

Network tomography has been widely used recently as an method to infer the network internal link-level characteristics by end-to-end measurement. In this paper, we consider the problem of estimating link loss rates using network tomography. The existing methods make the inference based on the whole tree of network, which is very complex for large scale network. To overcome this limitation, we propose a low complexity inference approach named LCIA. In the LCIA, we deploy monitors at internal nodes to reduce the complexity of inferring the link loss rates. It mainly consists of two steps. The first step is to deploy monitors at specific internal nodes to divide the original tree into several sub-trees with minimum depth. The second step is to infer the link loss rates of sub-trees by a new estimator which is an explicit function of loss measurements. The LCIA has the following features. First, it greatly reduces the inference complexity as the inference on the sub-trees is much simpler. Second, it improves the accuracy of the estimated results since the variance of loss estimator on sub-trees with lower depth is smaller than that on the original tree. The analytical and simulation results demonstrate that the LCIA outperforms the existing methods both on computation complexity and inference accuracy.

Accurate and Efficient Network Tomography Through Network Coding

“…Losses are therefore spatially and temporally independent. This model is commonly used in the literature [7]- [9], [14]- [16] for network tomography. We define α j ∈ (0, 1] as the link success rate of the jth link in set E I ∪ E V , which is the probability that a packet can successfully be transmitted on the jth link.…”

“…Although there are extensive studies of link loss inference on multicast tree topologies [7], [14]- [16], loss tomography on mesh topologies is still a challenging problem. Bu et al proposed an approach using multiple trees to cover a mesh topology and combine the inferred loss rates [17].…”

ExpoNET: A Flexible Platform for Concurrent Experiments on Programmable Infrastructure

“…By these records, we can calculate the results of link loss rate the message transmission delay of each link by our the expressions shown [20] and [21]. At the same time, we can obtain the actual network information of each link in time.…”

“…In Ref. [20] and [21], we have studied how to infer link loss rate and link transmission delay by network tomography, respectively. In these two papers, we just give simulation results since it is difficult to conduct experiments under the condition of that moment.…”

A Sort-Based Approach to Infer the Network Topology

“…Therefore the network tomography [1] has been proposed to infer the network topology without any corporation of internal network elements. It is used to infer the internal performance characteristics [10] [11] and discovery the network topology [3] by multicast probe packets from the source node to the receivers in the tree-structured network. The basic principle underlying network tomography is to extract the hidden information from active end-to-end measurement data by statistic inference.…”