Reduce MLC Software Costs by Optimizing LPAR Configurations

Todd HavekostBy Todd Havekost

A prominent theme among IT organizations today is an intense focus on expense reduction. For mainframe departments, this routinely involves seeking to reduce IBM Monthly License Charge (MLC) software expense, which commonly represents the single largest line item in their budget.

This is the second article in a four-part series focusing largely on a topic that has the potential to generate significant cost savings but which has not received the attention it deserves, namely processor cache optimization. (Read part one here).  Without an understanding of the vital role processor cache plays in CPU consumption and clear visibility into the key cache metrics in your environment, significant opportunities to reduce CPU consumption and MLC expense may not be realized.[1]

This article focuses on changes to LPAR configurations that can improve cache efficiency, as reflected in lower RNI values. The two primary aspects covered will be optimizing LPAR topology, and increasing the amount of work executing on Vertical High (VH) CPs through optimizing LPAR weights. Restating one of the key findings of the first article, work executing on VHs optimizes processor cache effectiveness, because its 1-1 relationship with a physical CP means it will consistently access the same processor cache.[2]

Continue reading

Mainframe Capacity “Through the Looking Glass”


By Todd Havekost


With the recent release of “Alice Through the Looking Glass” (my wife is a huge Johnny Depp fan), it seems only appropriate to write on a subject epitomized by Alice’s famous words:

“What if I should fall right through the center of the earth … oh, and come out the other side, where people walk upside down?”  (Lewis Carroll, Alice in Wonderland)

Along with the vast majority of the mainframe community, I had long embraced the perspective that running mainframes at high levels of utilization was essential to operating in the most cost-effective manner. Based on carefully constructed capacity forecasts, our established process involved implementing just-in-time upgrades designed to ensure peak utilization’s remained slightly below 90%.

It turns out we’ve all been wrong.  Continue reading