970 research outputs found

    Inferring functional connectivity from time-series of events in large scale network deployments

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    To respond rapidly and accurately to network and service outages, network operators must deal with a large number of events resulting from the interaction of various services operating on complex, heterogeneous and evolving networks. In this paper, we introduce the concept of functional connectivity as an alternative approach to monitoring those events. Commonly used in the study of brain dynamics, functional connectivity is defined in terms of the presence of statistical dependencies between nodes. Although a number of techniques exist to infer functional connectivity in brain networks, their straightforward application to commercial network deployments is severely challenged by: (a) non-stationarity of the functional connectivity, (b) sparsity of the time-series of events, and (c) absence of an explicit model describing how events propagate through the network or indeed whether they propagate. Thus, in this paper, we present a novel inference approach whereby two nodes are defined as forming a functional edge if they emit substantially more coincident or short-lagged events than would be expected if they were statistically independent. The output of the method is an undirected weighted graph, where the weight of an edge between two nodes denotes the strength of the statistical dependence between them. We develop a model of time-varying functional connectivity whose parameters are determined by maximising the model's predictive power from one time window to the next. We assess the accuracy, efficiency and scalability of our method on two real datasets of network events spanning multiple months and on synthetic data for which ground truth is available. We compare our method against both a general-purpose time-varying network inference method and network management specific causal inference technique and discuss its merits in terms of sensitivity, accuracy and, importantly, scalability

    Network Inference via the Time-Varying Graphical Lasso

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    Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

    A smartwater metering deployment based on the fog computing paradigm

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    In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system

    Big data analyses reveal patterns and drivers of the movements of southern elephant seals

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    The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with big data, that require no a priori assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for big data techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.Comment: 18 pages, 5 figures, 6 supplementary figure
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