David Merrill

Re-hydrating Cloud Economic Blogs - Part 4

Blog Post created by David Merrill on Oct 19, 2016

Economic Advantages of Pay-as-You-Grow or Utility Storage Services

by David Merrill on Feb 28, 2013

 

I am working in Australia for several weeks, and find that many sourcing companies (including HDS) have been in the Storage as a Service business for several years. Most companies are aware of these offerings and general acceptance seems to be higher here than in other parts of the world. Part of that may be that these national resources are here in-country, and a threat of data or systems moving off-continent seem to be less likely. The distinctions of utility services compared with traditional outsourcing are mostly well understood. Recently I  met with  a few customers who still have a bad recollection of old-fashioned outsourcing and are skeptical that these new consumption methods are really a disguise for bulky, inflexible outsourcing deals. They also do not see how these options can reduce real costs.

 

In this blog, I will outline the theory of storage cost savings with a utility (scale up and down) pay-as-you-go storage service. Let’s just call this “storage utility” for now. And for this blog let’s focus on the CAPEX impact of savings/differences.

I will start by describing an overly simplistic, multi-year storage growth model. First, let’s look at the written-to data requirements of a company.

david-feb-2013.bmp

In the above graph, we see several points of interest in the demand curve:

-       Point A is a steady-state growth with new projects and new infrastructure.

-       Point B represents a new project (perhaps an analytics event) where a lot of new data is introduced (machine-generated data) to be analyzed. This might be data that can reside in a lower-tier of storage, but will be on-line for several months.

-       At point C, the burst mode data goes away. Perhaps it is deleted; perhaps it is put back to tape or an archive. But the total capacity demand for written-to data drops.

-       At point D, there is a merger or acquisition, and the storage/data demand grows rapidly for a sustained period of time.

Next, let’s look at a traditional purchase model that would be required to meet the above demand.

Devid-feb-2.bmp

The top line represents a usable capacity rate needed to support the written-to levels in the first graph. In this chart let’s also assume:

-       Thin volumes have limited adaptation in the environment.

-       A 5-year depreciation for assets.

-       Once assets are purchased, they stay around until the end of the 5-year depreciation term

-       There is a lag between demand and delivery. This is due to the time it takes to scope, engineer, bid, purchase and install the assets.

-       Engineering with reserve capacity (20%) is common for the storage management team.

-       Utilization of data (written to) compared to allocated is an industry average 40%. Therefore, the white space or wasted capacity of what is allocated has to be added to the reserve capacity defined by the storage team.

As you can see, overall utilization is very poor. The spikes at the end of event B create pools of unutilized storage. As new projects come online, they want to have their own resources and not a hand-me down disk. Utilization rates that start at 30% of this model can easily drop to 15% in a short period of time. And finally, the written-to-raw ratio hovers around 1:6 (which would be very, very good).

Now let’s look at a storage utility approach to the same demand scenario. In this service:

-       Only thin provisioned volumes are delivered to the customer. In this example I have a conservative rate of 110% average oversubscription.

-       Capacity can scale up and down.

-       The lag between requirement and delivery is hours or days, not months.

-       There is no need for reserve capacity. The service provider keeps all the reserve so that the customer pays for only what they need.

-       White space within the allocated volumes may still exist, but over-provisioning will reduce most of this waste.

David-Feb-3.bmp

As you can see, the differences are tremendous. Not only is the total storage footprint different…

-       The written-to-raw ratio turns from 1:6 to around 3:5.

-       Very fast mean time to deliver provides positive impact to the projects.

-       Floor space, power and cooling costs are reduced by 35-50%.

-       With less equipment on the floor

  • Management costs are held in check
  • HW maintenance rates (even as part of the utility rate) are reduced

-       Agility in acquiring and de-commissioning IT assets can bring better business value, just-in-time OPEX spend in place of long term CAPEX commitments.

If you subtract the capacity of the Storage Utility line (green) from the BAU line (brown), you get a sense of the different in total capacity at a point in time that would be needed to meet the business needs of data storage.

Moving from a CAPEX spend to this OPEX storage utility may also present some internal finance and accounting challenges, which we can discuss in the next blog. But for the present view, reducing infrastructure, having the agility to consume what you need when needed, and having a variable rate cost align to business needs are some of the key benefits to this type of it service delivery. Other aspects, benefits and detriments will be covered in my next few blogs.

 

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