AI Reduces Risk In City Traffic and In z/OS Mainframe Availability
Beyond the context that individuals are free to circulate, AI algorithms on traffic cameras can locate events or individuals potentially dangerous for the population. Amongst others features, AI video analytics can identify drivers who are texting or are not observing traffic signs and who are at risk of provoking accidents. Such drivers are often repeat offenders.
Like urbanists who strive to improve circulation in a city, mainframe engineers have for years sought to improve the performance of peripheral hardware infrastructures in order to optimize z/OS processors’ MSU resources.
The numbers of 3390 logical volumes utilized have multiplied by thousands over the years but senior z/OS engineers specialized in I/O performance have retired. Yet, DASD and Tape I/O management has gained in significance. Response times are now measured in microseconds and working with weighted averages is not adequate to spot volumes causing availability risks.
Easy Analyze DASD Mainframe (EADM AI) is a machine learning solution that brings z/OS Storage and I/O expertise inside today’s IT shops, where teams are asked to do more with less. EADM analyzes data rich RMF and CMF files automatically and daily to inform on the what, where, why and when of availability risks, often caused by drifts in response time of critical 3390 volumes that store strategic Enterprise data files.
Whether in IBM, Hitachi Vantara or EMC/DELL DASD infrastructures, EADM keeps z/OS resiliency managers from guessing thanks to a prescriptive AI model that also recommends remediating tailor-fit z/OS features.
