This is part 2 of this blog. If you haven’t read the first section, you can read that here.
After being announced as a product in 1974, RMF was further expanded to provide more capabilities such as RMF Monitor 2 and RMF Monitor 3. These provided real time insight into the internal workings of z/OS to help understand and manage the performance of the z/OS infrastructure. The value of the RMF performance measurement data has been proven over the decades as it, or a compatible product from BMC named CMF, is used in every mainframe shop today. Many new record types have been added in recent years as the z/OS infrastructure capabilities continue to evolve.
A related product – Systems Management Facility or SMF – was originally created to provide resource usage information for chargeback purposes. SMF captured application usage statistics, but was not always able to capture the entire associated system overhead. Eventually, SMF and RMF were expanded to capture detailed statistics about all parts of the mainframe workloads and infrastructure operation, including details about third party vendor devices such as storage arrays. RMF and SMF now generate what is likely the most robust and detailed performance and configuration data of any commercial computing environment in the data center.
As the data sources to report on the performance of the workloads and the computer infrastructure grew, different performance tools were created to display and analyze the data. The information in the data was very complex and the total amount of data captured is overwhelming, creating challenges to identify performance problems. Typically, this requires analysts who have extensive insight into the specific infrastructure areas being analyzed, and an understanding of how they respond to different applications workloads. As applications have grown more complex, more real-time, with more platforms and components involved, the performance analysis task also has become more difficult.
To deal with the growing amount and complexity of data, and the need for better performance analysis, data warehouses to capture and store the data were introduced in the 80’s and 90’s. Vendors developed many different approaches to capturing data, modifying it to get it onto the same time basis and then create reports from it to highlight what was happening to the systems and applications.
As noted earlier, RMF and SMF have become the single point of data collection for applications, systems, sub-systems and components like special processors and storage subsystems. Mining this data and being able to do it with a high level of automation is a significant challenge. The performance database and reporting approaches developed decades ago that are still commonly in use today just do not provide the intelligence needed.
The key to managing z/OS environments is choosing the necessary RMF and SMF data, then having analysis capabilities to automatically highlight performance issues without having to spend days analyzing each day’s data. As everyone knows there are many products to gather and report on the information in this data, and these products have wide usage. However, finding the cause of performance or availability issues is very complex. The key to finding the causes is to have a way to correlate problem areas without having to have analysts to pour over thousands of pages of graphs or reports.
IntelliMagic’s entry into first the storage subsystem area, then moving up to evaluate entire systems, has made a significant step forward in automating the extremely time-consuming effort of data analysis. Today’s systems have become so complex that Artificial Intelligence (AI) is an absolute must. A slow response time or slow application throughput may have significant penalties on service level agreements, or in Cloud implementations, significant challenges to even determine the causes of poor performance.
As an IT professional involved in the genesis and evolution of performance measurement data and analysis on the mainframe platform, it has been a pleasure to see the effectiveness of IntelliMagic’s approach to finding what is relevant to the massive amount of data that is generated today.
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