Curbing our Appetite for Storage Growth – Part 2
by David Merrill on Aug 31, 2012
In my last post, I discussed the difficulty in accurately projecting new storage demand and the options available to the storage team to present new growth slopes with new/different storage architectures.
I tend to see organizations approach these new architectures (virtualization, thin provisioning, tiering, etc.) with an incremental-stepped approach. This ensures that the operational nature of these changes is put into place correctly, and that storage managers and storage consumers are all on the same page. Over time, the incremental approach to taking on new technologies will allow for a phased approach to reducing the overall storage appetite or demand. This can be depicted in the following chart using the previous growth slope options.
I also observe that some organizations(in my opinion, the best in class) also setup intermediate metrics to track their process. One of these metrics can be a written-to-raw ratio. The actual written capacity compared to raw (non RAID) storage capacity includes copy data, while the written TB may not. This can be an effective measurement for non-technical people to understand how much storage is needed to purchase to hold the written-amount [HDS1] of data. RAID overhead, white space, and un-used capacity all contribute to the differences between written and raw.
The options to reduce or change the growth slope all present different written-to-raw ratios. If the graphing exercise is too complicated of an explanation, you might prefer a simpler math exercise. Written-to-raw rations can help with technology improvement programs:
Current baseline rate is (for example) 1:10
- Thin provisioning can give us 1:4
- Compression can give us 1:5
- Virtualization and consolidation can get us 1:8
- Compounding some or all of these (they become additive, and somewhat mutually exclusive) can yield a 1:3 ratio
Metrics, graphics or numerics, are needed to demonstrate to management and users how the IT team is doing everything in its power to change the growth slope without introducing disruptive or complex new solutions. In tough situations some of these difficult decisions might be necessary, so the IT team needs to take care of all the low-hanging fruit options like tiering and de-dupe before taking on tougher actions like rationing or charge-back.
In my next and final post on this topic next week, I will explore modeling methods and worst-case planning to try to determine where your current infrastructure may break (at a certain growth level).
