Winter SHARE 208 was held in Sacramento, California the week of March 2th. Throughout the years the scope and tone of the SHARE conference has changed based upon the technologies everyone is discussing. In the past, it was focused on the mainframe and how to better manage your environment. This year, SHARE was focused on emerging technologies that can be used to manage and monitor your environment. The most talked about next-generation tech being and Artificial Intelligence. As I listened to the sessions, I it became clear that Machine is where many of the attendees were particularly focused.

Mainframe Meets Machine Learning and Artificial Intelligence at SHARE Sacramento - banner2

A Common Thread between SHARE and Computer Measurement Group

This is the fourth SHARE conference I have presented at with different organizations. For years, I have presented at a number of different conferences, both national and regional. Each conference has its own flavor and target audience. Many of the individuals that go to SHARE would not go to the Computer Measurement Group (CMG) event and vice versa. That said, this year, I came to present a topic that would be of interest at either event… the Changing Landscape of Capacity Management for the Mainframe. The basis of the topic is how Mainframe capacity managers need to ensure they are looking at the process from a business standpoint rather than just as a technical application.

Capacity Management’s Role in Machine Learning

So where does Capacity Management fit into the white hot new trend of Machine Learning? By definition, Machine Learning is “a field of computer science that gives computer systems the ability to “learn” with data, without being explicitly programmed.” Therefore, the key is to provide enough meaningful data for Machine Learning capabilities to automate into analytics. Capacity Management delivers a vast amount of information about IT resources and their utilization, including enabling machine learning programs to perform analytics in the background for the reporting of “Time to Live” until a resource is exhausted. The key to the performance of this analysis is the setting of thresholds, whether those thresholds are static or self-learned.

Self-Learning thresholds is where Capacity Managers want to get to within their environments. One of the discussions that was held at SHARE was focused on whether, with machine learning, we are eliminating the human factor. My answer to that statement is that we can never eliminate the human factor. There are dynamics within the environment that a machine cannot account for in its processing and learning. As we say many times, humans can do things we never have planned for with the software. There will continue to be analysis that must be performed on the reports to ensure a variable is not missed. Syncsort, with software products like Athene and Ironstream with Splunk is using this type of technology to enable this type of machine learning. Over the next twelve I think this area will be one with large scale growth. The key to making this work is to ensure the quality of the data that is gathered, and having it correlated with the technical and business data. The business and application view is what organizations are looking to display on dashboards for all levels within the enterprise.

One of the benefits of going to a conference is seeing what the are presenting in the exhibition hall along with meeting up with friends and former colleagues from the past. At each conference, you have the stalwarts including Syncsort, BMC, ASG, IntelliMagic, Velocity and IBM. Every year there are some new companies that show up at the shows. Throughout my long career, I have either worked with many people in the exhibition hall or know them from the . The nice feature about the technical aspect in the relationships is that we talk about the how our jobs are changing within the and the new technologies affecting us.

Conferences such as SHARE continue to provide a great venue for all attendees to share thoughts and insights about what’s changing and what technologies can help address the challenges and opportunities that come with that change.

Download our free eBook, “Mainframe Meets Machine Learning“, to learn about the most difficult challenges and issues facing mainframes today, and how the benefits of machine learning could help alleviate some of these issues.



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