Loom: complex large-scale visual insight for large hybrid IT infrastructure management

James Brook, Felix Cuadrado, Eric Deliot, Julio Guijarro, Rycharde J Hawkes, Marco Lotz, Romaric Pascal, Suksant Sae-Lor, Luis M. Vaquero*, Joan Varvenne, Lawrence Wilcock

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
322 Downloads (Pure)


Interactive visual exploration techniques (IVET) such as those advocated by Shneiderman and extreme scale visual analytics have successfully increased our understanding of a variety of domains that produce huge amounts of complex data. In spite of their complexity, IT infrastructures have not benefited from the application of IVET techniques.

Loom is inspired in IVET techniques and builds on them to tame increasing complexity in IT infrastructure management systems guaranteeing interactive response times and integrating key elements for IT management: Relationships between managed entities coming from different IT management subsystems, alerts and actions (or reconfigurations) of the IT setup. The Loom system builds on two main pillars: (1) a multiplex graph spanning data from different ITIMs; and (2) a novel visualisation arrangement: the Loom “Thread” visualisation model.

We have tested this in a number of real-world applications, showing that Loom can handle million of entities without losing information, with minimum context switching, and offering better performance than other relational/graph-based systems. This ensures interactive response times (few seconds as 90th percentile). The value of the “Thread” visualisation model is shown in a qualitative analysis of users’ experiences with Loom.
Original languageEnglish
Pages (from-to)47-62
Number of pages16
JournalFuture Generation Computer Systems
Early online date26 Aug 2017
Publication statusPublished - 1 Mar 2018


  • Cloud
  • Complexity
  • Extreme scale visual analytics
  • Management
  • Scale
  • Visual analytics
  • Visualisation


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