The analysis of computer and communication networks gives rise to some interesting inverse problems. This paper is concerned with active network tomography where the goal is to recover information about quality-of-service (QoS) parameters at the link level from aggregate data measured on end-to-end network paths. The estimation and monitoring of QoS parameters, such as loss rates and delays, are of considerable interest to network engineers and Internet service providers. The paper provides a review of the inverse problems and recent research on inference for loss rates and delay distributions. Some new results on parametric inference for delay distributions are also developed. In addition, a real application on Internet telephony is discussed.Comment: Published at http://dx.doi.org/10.1214/074921707000000049 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

As computer simulations continue to grow in size and complexity, they present a particularly challenging class of big data problems. at this scale can generate output that exceeds both the storage capacity and the bandwidth available for transfer to storage, making post-processing and analysis challenging. One approach is to embed some analyses in the simulation while the simulation is running -a strategy often called in situ analysis -to reduce the need for transfer to storage. Another strategy is to save only a reduced set of time steps rather than the full simulation. Typically the selected time steps are evenly spaced, where the spacing can be defined by the budget for storage and transfer. This paper combines both of these ideas to introduce an online in situ method for identifying a reduced set of time steps of the simulation to save. Our approach significantly reduces the data transfer and storage requirements, and it provides improved fidelity to the simulation to facilitate post-processing and reconstruction. We illustrate the method using a computer simulation that supported NASA's 2009 Lunar Crater Observation and Sensing Satellite mission.

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There has been considerable interest over the last few years in collecting and analyzing internet traffic data in order to estimate quality of service parameters such as packet loss rates and delay distributions. In this paper, we focus on fast and efficient estimation methods for network link delay distributions based on end-to-end measurements obtained by probing the underlying. We introduce a rigorous statistical framework for designing the necessary probing experiments and examine the properties of the proposed estimators. The proposed framework and the resulting methodology are validated using data collected on the University of North Carolina (UNC) local area network.

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