A Trace Macroscopic Description based on Time Aggregation

Damien Dosimont 1, * Lucas Mello Schnorr 2 Guillaume Huard 1, * Jean-Marc Vincent 3
* Corresponding author
1 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
3 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Today, because of computing system complexity, it is required to trace application executions to understand their behavior. Visualization techniques provide some help in representing their content, but their scalability is limited both because of human perception and bounded screen resolution. To solve this issue, we propose a visualization based on time aggregation that provides a concise overview of a trace whatever its size. The level of details in this visualization can be configurable by users who can adjust the compromise between concision (gain from aggregation) and information loss. They can then refine their analysis by zooming in an interesting part and choosing a less aggregated overview for this interesting part. This visualization is implemented in our tool, Ocelotl, which enables users to interact with this visualization by changing the selected time interval and its aggregation settings dynamically. The results presented in this paper show that the technique can help users correctly identify anomalies in very large trace files composed of up to forty million events.
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Damien Dosimont, Lucas Mello Schnorr, Guillaume Huard, Jean-Marc Vincent. A Trace Macroscopic Description based on Time Aggregation. [Research Report] RR-8524, INRIA. 2014. ⟨hal-00981020v2⟩

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