Fundamental limits of failure identifiability by Boolean Network Tomography

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by egde-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. The proposed upper bounds represent a fundamental limit on the identifiability of failures via Boolean network tomography. This analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real network

A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies

Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography

We study maximal identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. We prove tight upper and lower bounds on the maximal identifiability of failure nodes for specific classes of network topologies, such as trees and $d$-dimensional grids, in both directed and undirected cases. We prove that directed $d$-dimensional grids with support $n$ have maximal identifiability $d$ using $2d(n-1)+2$ monitors; and in the undirected case we show that $2d$ monitors suffice to get identifiability of $d-1$. We then study identifiability under embeddings: we establish relations between maximal identifiability, embeddability and graph dimension when network topologies are model as DAGs. Our results suggest the design of networks over $N$ nodes with maximal identifiability $\Omega(\log N)$ using $O(\log N)$ monitors and a heuristic to boost maximal identifiability on a given network by simulating $d$-dimensional grids. We provide positive evidence of this heuristic through data extracted by exact computation of maximal identifiability on examples of small real networks

Design and Analysis of Distributed Faulty Node Detection in Networks

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design. PRD assigns weights to individual links as well as end-to-end delay, so as to reflect the node status in the long run of the network. Large-scale simulation results demonstrate that PRD performs better than the widely used ETX metric as well as other two metrics devised recently in terms of energy consumption and end-to-end delay, while guaranteeing packet delivery ratio.

Cooperative Localization on Computationally Constrained Devices

Cooperative localization is a useful way for nodes within a network to share location information in order to better arrive at a position estimate. This is handy in GPS contested environments (indoors and urban settings). Most systems exploring cooperative localization rely on special hardware, or extra devices to store the database or do the computations. Research also deals with specific localization techniques such as using Wi-Fi, ultra-wideband signals, or accelerometers independently opposed to fusing multiple sources together. This research brings cooperative localization to the smartphone platform, to take advantage of the multiple sensors that are available. The system is run on Android powered devices, including the wireless hotspot. In order to determine the merit of each sensor, analysis was completed to determine successes and failures. The accelerometer, compass, and received signal strength capability were examined to determine their usefulness in cooperative localization. Experiments at meter intervals show the system detected changes in location at each interval with an average standard deviation of 0.44m. The closest location estimates occurred at 3m, 4m and 6m with average errors of 0.15m, 0.11m, and 0.07m respectively. This indicates that very precise estimates can be achieved with an Android hotspot and mobile nodes

Node Failure Localization via Network Tomography

We investigate the problem of localizing node failures in a communication network from end-to-end path measure-ments, under the assumption that a path behaves normally if and only if it does not contain any failed nodes. To uniquely localize node failures, the measurement paths must show dif-ferent symptoms under different failure events, i.e., for any two distinct sets of failed nodes, there must be a measure-ment path traversing one and only one of them. This condi-tion is, however, impractical to test for large networks. Our first contribution is a characterization of this condition in terms of easily verifiable conditions on the network topol-ogy with given monitor placements under three families of probing mechanisms, which differ in whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the de-fault routing protocol). Our second contribution is a char-acterization of the maximum identifiability of node failures, measured by the maximum number of simultaneous failures that can always be uniquely localized. Specifically, we bound the maximal identifiability from both the upper and the lower bounds which differ by at most one, and show that these bounds can be evaluated in polynomial time. Finally, we quantify the impact of the probing mechanism on the capability of node failure localization under different prob-ing mechanisms on both random and real network topolo-gies. We observe that despite a higher implementation cost, probing along controllable paths can significantly improve a network’s capability to localize simultaneous node failures

FAst in-network GraY failure detection for ISPs

Avoiding packet loss is crucial for ISPs. Unfortunately, malfunctioning hardware at ISPs can cause long-lasting packet drops, also known as gray failures, which are undetectable by existing monitoring tools. In this paper, we describe the design and implementation of FANcY, an ISP-targeted system that detects and localizes gray failures quickly and accurately. FANcY complements previous monitoring approaches, which are mainly tailored for low-delay networks such as data center networks and do not work at ISP scale. We experimentally confirm FANcY's capability to accurately detect gray failures in seconds, as long as only tiny fractions of traffic experience losses. We also implement FANcY in an Intel Tofino switch, demonstrating how it enables fine-grained fast rerouting