AI and z/OS Performance and Capacity Analysis: 2018 Predictions

Brent PhillipsBy Brent Phillips2018 Predictions on AI and z/OS Performance and Capacity Analysis

2018 is gearing up to be a watershed year for z/OS performance and capacity professionals.

Industry analysts have been talking for some years now about Artificial Intelligence (AI) and the role it will play in our work. But what that truly means, and its value in day-to-day operations has not yet been understood or realized by most professionals in this field.

There are many different types of AI, but not all are useful in making the computer do the kind of infrastructure performance and availability health assessment work that is no longer feasible for human analysts to proactively do every day. But when properly designed and deployed, it has proven very effective to implement automated, AI-driven decision making about what all the data means for identifying current or near-term performance problems and their root-causes.

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5 Reasons IBM z/OS Infrastructure Performance & Capacity Problems are Hard to Predict and Prevent

Brent PhillipsBy Brent Phillips

Solving z/OS infrastructure performance and capacity problems is difficult. Getting ahead of performance and capacity problems before they occur and preventing them is more difficult still. This is why it takes years, and decades even, for performance analysts and capacity planners to become experts.

And together, with the rapid retiring of the current experts, the difficulty in becoming an expert is why the performance and capacity discipline for the mainframe is experiencing a significant skills gap. It is simply too difficult and time consuming to understand what the data means for availability, let alone derive predictive intelligence about upcoming production problems within the complex IBM z Systems infrastructure.

The primary root causes of this performance and capacity management problem are:

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6 Signs You Already Have a Skills Gap for z/OS Performance and Capacity Planning

Brent PhillipsBy Brent PhillipsSigns you have a skills gap for z/os performance and capacity planning

The mainframe skills gap is a well-known issue, but most of the focus is on mainframe application development. A large z/OS mainframe organization may have thousands of application developers but only 20 or fewer performance & capacity planning staff. Even though fewer in number, these IT staff have an outsized impact on the organization.

The problem, however, is not just about recruiting new IT staff members to the team. The road to becoming a true z/OS performance and capacity (perf/cap) expert is far longer and more difficult than what is necessary for a programmer to learn to code in a mainframe programming language like COBOL. Consequently, it is not feasible to fill the performance and capacity planning gap with new recruits, and recruiting experienced staff from the short supply is difficult. Even teams that have all the headcount positions filled very often exhibit at least some of the signs that they are being negatively impacted by insufficient levels of expert staff.

A primary contributor to the problem is the antiquated way of understanding the RMF and SMF performance data that most sites still use. The way this data is processed and interpreted not only makes it difficult for new IT staff to learn the job, but it also makes the job for the existing experts more difficult and time consuming.

Here are six signs that indicate your z/OS performance and capacity team would benefit by modernizing analytics for your infrastructure performance and configuration data.

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