Section: New Results

Vizualisation

We have proposed a methodology for detecting resource usage anomalies in large scale distributed systems. The methodology relies on four functionalities: characterized trace collection, multi-scale data aggregation, specifically tailored user interaction techniques, and visualization techniques. We have shown the efficiency of this approach through the analysis of simulations of the volunteer computing Berkeley Open Infrastructure for Network Computing architecture (BOINC). Three scenarios have been analyzed in [48] , [23] : analysis of the resource sharing mechanism, resource usage considering response time instead of throughput, and the evaluation of input file size on Berkeley Open Infrastructure for Network Computing architecture. The results show that our methodology enables to easily identify resource usage anomalies, such as unfair resource sharing, contention, moving network bottlenecks, and harmful short-term resource sharing. Triva, the resulting software, has been demonstrated at the SuperComputing conference.

We also have investigated how to use trace-based visualization to understand applications I/O performance [49] and how to visually compare two traces [70] and highlight differences.