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

• Published in: Future generation computer systems Vol. 80; pp. 47 - 62
• Main Authors: Brook, James, Cuadrado, Felix, Deliot, Eric, Guijarro, Julio, Hawkes, Rycharde, Lotz, Marco, Pascal, Romaric, Sae-Lor, Suksant, Vaquero, Luis M., Varvenne, Joan, Wilcock, Lawrence
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2018
• Subjects: Cloud; Complexity; Extreme scale visual analytics; Management; Scale; Visual analytics; Visualisation; Extreme scale visual analytics; Visual analytics; Cloud; Visualisation; Scale; Management; Complexity
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2017.08.013

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. •Interactive insight extraction techniques need to be extended for manageability software.•An extended multiplex graph powers the “Thread” visualisation model.•The extended multiplex graph delivers interactive response times (few seconds).•Loom excels at aggregating data and on relational queries to these aggregations.•It enables visual interactive spotting of relationships between aggregations of managed entities.