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Analysis of institutional authors

Duenas, Juan C.AuthorNavarro, Jose M.AuthorParada G, Hugo A.AuthorAndion, JavierAuthorCuadrado, FelixAuthor

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June 9, 2019
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Applying Event Stream Processing to Network Online Failure Prediction

Publicated to: IEEE COMMUNICATIONS MAGAZINE. 56 (1): 166-170 - 2018-01-01 56(1), DOI: 10.1109/MCOM.2018.1601135

Authors:

Dueñas, JC; Navarro, JM; Parada, HA; Andión, J; Cuadrado, F
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Affiliations

Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England - Author
Univ Politecn Madrid, Madrid, Spain - Author

Abstract

Predicting failures on networks and systems is critical in order to maintain high uptime rates. Online failure prediction (OFP) techniques use machine learning and predictive analytics to generate failure models that can be applied to computer network data. These techniques can be provisioned on state-of-the-art stream processing systems, such as Spark Streaming, in order to cope with the scalability challenges from the base data. A big challenge with OFP is selecting the right information to process, as well as the appropriate features in order to achieve high accuracy in predicting failures on complex, interconnected systems. In this article we describe an OFP system built over Apache Spark that takes a repository of network management events, trains a Random Forest model, and uses this model to predict the appearance of future events in near real time. We show through our experiments the usefulness of network management events for accurate predictions, and the advantages of the proposed system in terms of predictive quality, cost, and ease of deployment.
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Keywords

Management

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE COMMUNICATIONS MAGAZINE due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2018, it was in position 7/266, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • Google Scholar: 26
  • WoS: 9
  • Scopus: 12
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Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/87188/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 148
  • Downloads: 1
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (DUEÑAS LOPEZ, JUAN CARLOS) and Last Author (CUADRADO LATASA, FELIX AURELIO).

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Awards linked to the item

The authors would like to express their gratitude to Produban, who inspired and motivated this challenge as a real business case and provided all necessary assistance to carry out this work. The work performed by Jose M. Navarro has been funded by Ministerio de Educacion de Espana under grant BFPU-2014-03209.
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