Making Use of Artificial Intelligence for IT Operations Analytics / AIOps

B._Phillips-web0By Brent PhillipsArtificial Intelligence for IT Operations Analytics

Enterprise computing systems and storage operations teams have a difficult job: manage the IT infrastructure so that application availability is always efficiently maintained. But this is virtually impossible due to the complexity and disparity of the meta-data and reporting tools for all the various infrastructure components. A lack of information is not the problem, rather the great need is to derive meaningful intelligence out of all the information.

But the cloud, for example, will not work for all applications due to performance and security requirements. And outsourcing doesn’t make infrastructure performance problems go away, in fact it can make them harder to resolve. So most enterprise organizations will still benefit from and require deep infrastructure performance analysis capabilities.

In recent years, a new class of products initially called IT Operations Analytics (ITOA) have come on the market with the design objective of providing a single interface into all the data generated from disparate devices, and more importantly, helping interpret what it really means for performance, availability, and efficiency.

The idea is to employ the computer to do more of the work of deriving meaningful intelligence out of all the data. If designed correctly, this is a type of artificial intelligence which is done by the machine and enables human IT operations teams to be more effective. In 2017 Gartner coined the term AIOps which is a nice nomenclature for the capability.

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

B._Phillips-web0By 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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