We present a visual analytics approach to support the workload management process for z/OS mainframes at IBM. This process typically requires the analysis of records consisting of 100 to 150 performance-related metrics, sampled over time. We aim at replacing the previous spreadsheet-based workflow with an easier, faster, and more scalable one regarding measurement periods and collected performance metrics. To achieve this goal, we collaborate with a developer embedded at IBM in a formative process. Based on that experience, we discuss the application background and formulate requirements to support decision making based on performance data for large-scale systems. Our visual approach helps analysts find outliers, patterns, and relations between performance metrics by data exploration through various visualizations. We demonstrate the usefulness and applicability of line plots, scatter plots, scatter plot matrices, parallel coordinates, and correlation matrices for workload management. Finally, we evaluate our approach in a qualitative user study with IBM domain experts.
%0 Conference Paper
%1 Schulz2017Visual
%A Schulz, Christoph
%A Rodrigues, Nils
%A Damarla, Krishna
%A Henicke, Andreas
%A Weiskopf, Daniel
%B Proceedings of the SIGGRAPH Asia Symposium on Visualization
%D 2017
%I ACM
%K 2017 A01 B01 from:leonkokkoliadis sfbtrr161 visus visus:rodrigns visus:schulzch visus:weiskopf
%P 4:1-4:7
%T Visual Exploration of Mainframe Workloads
%U http://dx.doi.org/10.1145/3139295.3139312
%X We present a visual analytics approach to support the workload management process for z/OS mainframes at IBM. This process typically requires the analysis of records consisting of 100 to 150 performance-related metrics, sampled over time. We aim at replacing the previous spreadsheet-based workflow with an easier, faster, and more scalable one regarding measurement periods and collected performance metrics. To achieve this goal, we collaborate with a developer embedded at IBM in a formative process. Based on that experience, we discuss the application background and formulate requirements to support decision making based on performance data for large-scale systems. Our visual approach helps analysts find outliers, patterns, and relations between performance metrics by data exploration through various visualizations. We demonstrate the usefulness and applicability of line plots, scatter plots, scatter plot matrices, parallel coordinates, and correlation matrices for workload management. Finally, we evaluate our approach in a qualitative user study with IBM domain experts.
@inproceedings{Schulz2017Visual,
abstract = {We present a visual analytics approach to support the workload management process for z/OS mainframes at IBM. This process typically requires the analysis of records consisting of 100 to 150 performance-related metrics, sampled over time. We aim at replacing the previous spreadsheet-based workflow with an easier, faster, and more scalable one regarding measurement periods and collected performance metrics. To achieve this goal, we collaborate with a developer embedded at IBM in a formative process. Based on that experience, we discuss the application background and formulate requirements to support decision making based on performance data for large-scale systems. Our visual approach helps analysts find outliers, patterns, and relations between performance metrics by data exploration through various visualizations. We demonstrate the usefulness and applicability of line plots, scatter plots, scatter plot matrices, parallel coordinates, and correlation matrices for workload management. Finally, we evaluate our approach in a qualitative user study with IBM domain experts.},
added-at = {2020-01-10T13:25:31.000+0100},
author = {Schulz, Christoph and Rodrigues, Nils and Damarla, Krishna and Henicke, Andreas and Weiskopf, Daniel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fdffb8a3b771f0378f76d1de030bdde8/sfbtrr161},
booktitle = {Proceedings of the SIGGRAPH Asia Symposium on Visualization},
interhash = {fe5ef55667ea4c424c65624e42424390},
intrahash = {fdffb8a3b771f0378f76d1de030bdde8},
keywords = {2017 A01 B01 from:leonkokkoliadis sfbtrr161 visus visus:rodrigns visus:schulzch visus:weiskopf},
pages = {4:1-4:7},
publisher = {ACM},
timestamp = {2020-10-05T11:56:45.000+0200},
title = {Visual Exploration of Mainframe Workloads},
url = {http://dx.doi.org/10.1145/3139295.3139312},
venue = {Article No. 4},
year = 2017
}