Stream: Virtual Room 5
Time: 10:00 - 10:45
Mainframe performance, capacity and cost management is traditionally based on analysis of historical data, especially SMF Data. Often this analysis results in insights that come too late to be useful. E.g., running a test job during peak hours yesterday caused an MSU spike that had a significant impact on cost and performance. At best this reactive approach leads to lessons learned that can mean better decisions in the future. But there are many cases where, if the analysis had been done in near real time, it would have been possible to act and avoid costs or performance problems. In this session, we explore use cases for this kind of proactive analysis including what problems can be addressed, what data is needed, what thresholds should be monitored, what actions can be taken, and what are some of the challenges in making this happen. Join us for a deep dive into leveraging near real-time SMF insights to drive smarter, more agile mainframe capacity, performance, and cost management.
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Steven Thomas is Chief Technology Officer at SMT Data, a company specialising in applying business intelligence tools and methods to capacity and performance data for large IT installations. Steven is responsible for product development and professional services at SMT Data. He has a Master’s Degree in Computer Science from Stanford University and 30 years’ experience from IBM, Fidelity Information Services and Saxo Bank prior to joining SMT Data in 2011.
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