Presenter: Tom Quinn
Few mainframe shops can afford the luxury of a dedicated performance monitoring team. Those that do, dedicate their time to learn system behaviors and set meaningful thresholds to watch for out of bounds metrics indicating a problem has, or is about to occur. This is a very time-consuming effort that requires regular review and maintenance to avoid the constant “that light is always red, ignore it” scenarios. Shops that do not have a dedicated team may set some thresholds on critical workloads or system components, but rarely get the opportunity to consistently review those thresholds; for example, a CPU threshold set three hardware versions ago is now obsolete. What if you could utilize Machine Learning to automate this process? What if your system could learn from your data? Learn what is normal on a Monday morning or a Thursday night?
In this session, come learn how Machine Learning can relieve you of this workload and let you focus on actually tuning the workloads, not just fighting fires.
About the presenter:
Tom Quinn is a Sr Engineering Services Architect at CA technologies with expertise in mainframe performance monitoring and management. Tom joined CA in 2009, bringing with him over 15 years of technical and leadership experience in Information Technology. Having worked for a large Mid-Western insurance company in mainframe system and application performance, Tom understands the technical challenges a large organization can face and utilizes his knowledge and experience to help IT teams be successful. You can reach him at [email protected]