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.Index Terms-Network management, network events, functional connectivity inference.

Abstract-ISP and commercial networks are complex and thus difficult to characterise and manage. Network operators rely on a continuous flow of event log messages to identify and handle service outages. However, there is little published information about such events and how they are typically exploited. In this paper, we describe in as much detail as possible the event logs and network topology of a major commercial network. Through analysing the network topology, textual information of events and time of events, we highlight opportunities and challenges brought by such data. In particular, we suggest that the development of methods for inferring functional connectivity could unlock more of the informational value of event log messages and assist network management operators.

An investigation of the compressibility factors of gaseous mixtures of carbon dioxide and helium has been carried out at 30°C, using the Burnett method. The pressure measuring system included a visual high-pressure manometer. Results are expressed in an empirical regression formula from which approximations to the virial coefficients can be obtained. The interaction coefficient characteristic of bimolecular encounters between helium and carbon dioxide is in reasonably good agreement with the value calculated by Lunbeck and Boerboom from the properties of the pure gases alone.

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An instrument is described for measuring the dynamic rheological properties of viscoelastic liquids and solids. The design of this instrument is based on that of Goldberg and Sandvik, but differs from theirs in that it is not limited to use at a point of resonance. Examples are given of results obtained by its use on a Newtonian liquid, a viscoelastic liquid, and a viscoelastic solid. A derivation of the equation governing the instrument when used for solids is given in an appendix.