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

Messager, Antoine, Parisis, George, Kiss, Istvan Z, Harper, Robert, Tee, Phil and Berthouze, Luc (2019) Inferring functional connectivity from time-series of events in large scale network deployments. IEEE Transactions on Network and Service Management, 16 (3). pp. 857-870. ISSN 1932-4537

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Abstract

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.

Item Type: Article
Keywords: network management, network events, functional connectivity inference
Schools and Departments: School of Engineering and Informatics > Informatics
School of Mathematical and Physical Sciences > Mathematics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication > TK5105.5 Computer networks
Depositing User: Luc Berthouze
Date Deposited: 05 Aug 2019 14:25
Last Modified: 03 Dec 2019 09:45
URI: http://sro.sussex.ac.uk/id/eprint/85312

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A fast method for calculating the proximity matrix in a large-scale dynamic networkG1742MOOGSOFT INCAgreement dated 17 December 2014