2711718
Tomography-based overlay network monitoring
Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with n end hosts, existing systems either require O(n2) measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Unlike other network tomography systems, we characterize end-to-end losses (this extends to any additive metrics, including latency) rather than individual link losses. We find a minimal basis set of k linearly independent paths that can fully describe all the O(n,2) paths. We selectively monitor and measure the loss rates of these paths, then apply them to estimate the loss rates of all other paths. By extensively studying synthetic and real topologies, we find that for reasonably large n (e.g., 100), k is only in the range of O(n log n). This is explained by the moderately hierarchical nature of Internet routine.Our scheme only assumes the knowledge of underlying IP topology, and any link can become lossy or return to normal. In addition, our technique is tolerant to topology measurement inaccuracies, and is adaptive to topology changes.
10897641
Reversible sketches for efficient and accurate change detection over network data streams
Traffic anomalies such as failures and attacks are increasing in frequency and severity, and thus identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows and looks for heavy changes in traffic patterns (<i>e.g.</i>, volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is not scalable. The recently proposed sketch-based schemes [14] are among the very few that can detect heavy changes and anomalies over massive data streams at network traffic speeds. However, sketches do not preserve the key (<i>e.g.</i>, source IP address) of the flows. Hence, even if anomalies are detected, it is difficult to infer the culprit flows, making it a big practical hurdle for online deployment. Meanwhile, the number of keys is too large to record.
 To address this challenge, we propose efficient <i>reversible hashing</i> algorithms to infer the keys of culprit flows from sketches without storing any explicit key information. No extra memory or memory accesses are needed for recording the streaming data. Meanwhile, the heavy change detection daemon runs in the background with space complexity and computational time sublinear to the key space size. This short paper describes the conceptual framework of the reversible sketches, as well as some initial approaches for implementation. See [23] for the optimized algorithms in details. comment We further apply various emph IP-mangling algorithms and emph bucket classification methods to reduce the false positives and false negatives. Evaluated with netflow traffic traces of a large edge router, we demonstrate that the reverse hashing can quickly infer the keys of culprit flows even for many changes with high accuracy.
166109
End-to-End Inference of Router Packet Forwarding Priority
Packet forwarding prioritization (PFP) in routers is one of the mechanisms commonly available to network administrators. PFP can have a significant impact on the performance of applications, the accuracy of measurement tools' results and the effectiveness of network troubleshooting procedures. Despite their potential impact, no information on PFP settings is readily available to end users. In this paper, we present an end-to-end approach for packet forwarding priority inference and its associated tool, POPI. This is the first attempt to infer router packet-forwarding priority through end-to-end measurement. Our POPI tool enables users to discover such network policies through the monitoring and rank classification of loss rates for different packet types. We validated our approach via statistical analysis, simulation, and wide-area experimentation in PlanetLab. As part of our wide-area experiments, we employed POPI to analyze 156 random paths across 162 PlanetLab nodes. We discovered 15 paths flagged with multiple priorities, 13 of which were further validated through hop-by-hop loss rates measurements. In addition, we surveyed all related network operators and received responses for about half of them confirming our inferences.
61999607
Towards unbiased end-to-end network diagnosis
Internet fault diagnosis is extremely important for end users, overlay network service providers (like Akamai [1]) and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel Least-biased End-to-end Network Diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSes and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties, and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSes, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity.
10399949
Quantifying Network Denial of Service: A Location Service Case Study
Network Denial of Service (DoS) attacks are increasing in frequency, severity and sophistication, making it desirable to measure the resilience of systems to DoS attacks. In this paper, we propose a simulation-based methodology and apply it to attacks on object location services such as DNS. Our results allow us to contrast the DoS resilience of three distinct architectures for object location.
887532
A Suite of Schemes for User-Level Network Diagnosis without Infrastructure
It is highly desirable and important for end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications. However, existing tools are inaccurate to infer the link-level loss rates and have large diagnosis granularity (in terms of the number of hops). To address these problems, we propose a suite of user-level diagnosis approaches in two categories: (1) only need to be deployed at the source and (2) deployed at both source and destination. For the former, we propose two fragmentation aided diagnosis approaches (FAD), Algebraic FAD and Opportunistic FAD, which uses IP fragmentation to enable accurate link-level loss rate inference. For the latter category, we propose Striped Probe Analysis (SPA) which significantly improves the diagnosis granularity over those of the source-only approaches. Internet experiments are applied to evaluate each individual schemes (including an improved version of the state-of-the-art tool, Tulip [1]) and various hybrid approaches. The results indicate that our approaches dramatically outperform existing work (especially for diagnosis granularity) and provide not only the best performance but also smooth tradeoff among deployment requirement, diagnosis accuracy and granularity.
31770762
Hierarchical Segmentation and Representation with Dynamic Link Architecture Neural Network
A segmentation scheme based on tracing objects through scale space is proposed. For analyzing the image structure in scale space, a hierarchical neuraloscillator network is proposed. Its intrinsic dynamics provides a natural and reliable representation of multiple segmented patterns. The pyramid-like structure and the local interactions between the neurons can easily integrate the local context information of pixels and global image structure. This model assumes that neural cells show oscillatory activities and the segmentation result is coded by synchronization of activities. This representation framework for neural network suggested by time correlation theory receives direct experimental support from neurophysiology experiment. Computer simulations demonstrate that the network can detect the topological character of images within several oscillatory cycles. Experiment on real image showed the model's ability of segmentation.
393866
Adaptive discretization for probabilistic model building genetic algorithms
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (ECGA), named real-coded ECGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD.
5569010
A Fast Efficient Parallel Hough Transform Algorithm on LARPBS
A parallel algorithm for Hough transform on a linear array with reconfigurable pipeline bus system (LARPBS) is presented. Suppose the number of θ-values to be considered is m, for an image with n × n pixels, the algorithm can complete Hough transform in O(1) time using mn2 processors and achieve optimal speed and efficiency. We also illustrate how to partition data and perform the algorithm on a LARPBS with fewer than mn2 processors, and hence show that the algorithm is highly scalable.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
17475296
Statistical geometrical features for texture classification
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method's performance under additive noise is also shown to be the best of the four.