PerfViz: a visualization tool for analyzing, exploring, and comparing storage controller performance data

Amit P. Sawant; Matti Vanninen; Christopher G. Healey

doi:10.1117/12.704603

29 January 2007 PerfViz: a visualization tool for analyzing, exploring, and comparing storage controller performance data

Amit P. Sawant, Matti Vanninen, Christopher G. Healey

Author Affiliations +

Proceedings Volume 6495, Visualization and Data Analysis 2007; 649507 (2007) https://doi.org/10.1117/12.704603
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

This paper presents a technique that allows viewers to visually analyze, explore, and compare a storage controller's performance. We present an algorithm that visualizes storage controller's performance metrics along a traditional 2D grid or a linear space-filling spiral. We use graphical "glyphs" (simple geometric objects) that vary in color, spatial placement and texture properties to represent the attribute values contained in a data element. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions. We identified four important goals for our project: 1. Design a graphical glyph that supports flexibility in its placement, and in its ability to represent multidimensional data elements. 2. Build an effective visualization technique that uses glyphs to represent the results gathered from running different tests on the storage controllers by varying their performance parameters. 3. Build an effective representation to compare the performance of storage controller(s) during different time intervals. 4. Work with domain experts to select properties of storage controller performance data that are most useful to visualize.

Citation Download Citation

Amit P. Sawant, Matti Vanninen, and Christopher G. Healey "PerfViz: a visualization tool for analyzing, exploring, and comparing storage controller performance data", Proc. SPIE 6495, Visualization and Data Analysis 2007, 649507 (29 January 2007); https://doi.org/10.1117/12.704603

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