This paper proposes a network tomography scheme for information-centric networking (ICN), which we call ICN tomography. When content is received over a conventional IP network, the communication occurs after converting the content name into an IP address, which is the locator, so as to identify the position of the network. By contrast, in ICN, communication is achieved by directly specifying the content name or content ID. The content is sent to the requesting user by a nearby node having the content or cache, making it difficult to apply a conventional network tomography that uses end-to-end quality of service (QoS) measurements and routing information between the source and destination node pairs as input to the ICN. This is because, in ICN, the end-to-end flow for an end host receiving some content can take various routes; therefore, the intermediate and source nodes can vary. In this paper, we first describe the technical challenges of applying network tomography to ICN. We then propose ICN tomography, where we use the content name as an endpoint to define an end-to-end QoS measurement and a routing matrix. In defining the routing matrix, we assume that the end-to-end flow follows a probabilistic routing. Finally, the effectiveness of the proposed method is evaluated through a numerical analysis and simulation.

In this paper, we consider network monitoring techniques to estimate communication qualities in wide-area mobile networks, where an enormous number of heterogeneous components such as base stations, routers, and servers are deployed. We assume that average delays of neighboring base stations are comparable, most of servers have small delays, and delays at core routers are negligible. Under these assumptions, we propose Heterogeneous Delay Tomography (HDT) to estimate the average delay at each network component from end-to-end round trip times (RTTs) between mobile terminals and servers. HDT employs a crowdsourcing approach to collecting RTTs, where voluntary mobile users report their empirical RTTs to a data collection center. From the collected RTTs, HDT estimates average delays at base stations in the Graph Fourier Transform (GFT) domain and average delays at servers, by means of Compressed Sensing (CS). In the crowdsourcing approach, the performance of HDT may be degraded when the voluntary mobile users are unevenly distributed. To resolve this problem, we further extend HDT by considering the number of voluntary mobile users. With simulation experiments, we evaluate the performance of HDT.

The increased performance of mobile terminals has made it feasible to collect data using users' terminals. By making the best use of the network performance data widely collected in this way, network operators should deeply understand the current network conditions, identify the performance-degraded components in the network, and estimate the degree of their performance degradation. For their demands, one powerful solution with such end-to-end data measured by users' terminals is network tomography. Meanwhile, with the advance of network virtualization by software-defined networking, routing is dynamically changed due to congestion or other factors, and each end-to-end measurement flow collected from users may pass through different paths between even the same origin-destination node pair. Therefore, it is difficult and costly to identify through which path each measurement flow has passed, so it is also difficult to naively apply conventional network tomography to such networks where the measurement paths cannot be uniquely determined. We propose a novel network tomography for the networks with undeterministic routing where the measurement flows pass through multiple paths in spite of the origin-destination node pair being the same. The basic idea of our method is to introduce routing probability in accordance with the aggregated information of measurement flows. We present two algorithms and evaluate their performances by comparing them with algorithms of conventional tomography using determined routing information. Moreover, we verify that the proposed algorithms are applicable to a more practical network.