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EPA Energy Star for Data Center Storage Update

Following up on a recent post about Green IT, energy efficiency and optimization for servers, storage and more, here are some additional thoughts, perspectives along with industry activity around the U.S. Environmental Protection Agency (EPA) Energy Star for Server, Data Center Storage and Data Centers.

First a quick update, Energy Star for Servers is in place with work now underway on expanding and extending beyond the first specification. Second is that Energy Star for Data Center storage definition is well underway including a recent workshop to refine the initial specification along with discussion for follow-on drafts.

Energy Star for Data Centers is also currently undergoing definition which is focused more on macro or facility energy (notice I did not say electricity) efficiency as opposed to productivity or effectiveness, items that the Server and Storage specifications are working towards.

Among all of the different industry trade or special interests groups, at least on the storage front the Storage Networking Industry Association (SNIA) Green Storage Initiative (GSI) and their Technical Work Groups (TWG) have been busily working for the past couple of years on taxonomies, metrics and other items in support of EPA Energy Star for Data Center Storage.

A challenge for SNIA along with others working on related material pertaining to storage and efficiency is the multi-role functionality of storage. That is, some storage simply stores data with little to no performance requirements while other storage is actively used for reading and writing. In addition, there are various categories, architectures not to mention hardware and software feature functionality or vendors with different product focus and interests.

Unlike servers that are either on and doing work, or, off or in low power mode, storage is either doing active work (e.g. moving data), storing in-active or idle data, or a combination of both. Hence for some, energy efficiency is about how much data can be stored in a given footprint with the least amount of power known as in-active or idle measurement.

On the other hand, storage efficiency is also about using the least amount of energy to produce the most amount of work or activity, for example IOPS or bandwidth per watt per footprint.

Thus the challenge and need for at least a two dimensional model looking at, and reflecting different types or categories of storage aligned for active or in-active (e.g. storing) data enabling apples to apples, vs. apples to oranges comparison.

This is not all that different from how EPA looks at motor vehicle categories of economy cars, sport utility, work or heavy utility among others when doing different types of work, or, in idle.

What does this have to do with servers and storage?

Simple, when a server powers down where does its data go? That’s right, to a storage system using disk, ssd (RAM or flash), tape or optical for persistency. Likewise, when there is work to be done, where does the data get read into computer memory from, or written to? That’s right, a storage system. Hence the need to look at storage in a multi-tenant manner.

The storage industry is diverse with some vendors or products focused on performance or activity, while others on long term, low cost persistent storage for archive, backup, not to mention some doing a bit of both. Hence the nomenclature of herding cats towards a common goal when different parties have various interests that may conflict yet support needs of various customer storage usage requirements.

Figure 1 shows a simplified, streamlined storage taxonomy that has been put together by SNIA representing various types, categories and functions of data center storage. The green shaded areas are a good step in the right direction to simplify yet move towards realistic and achievable befits for storage consumers.

Figure 1 Source: EPA Energy Star for Data Center Storage web site document

The importance of the streamlined SNIA taxonomy is to help differentiate or characterize various types and tiers of storage (Figure 2) products facilitating apples to apples comparison instead of apples or oranges. For example, on-line primary storage needs to be looked at in terms of how much work or activity per energy footprint determines efficiency.

Figure 2: Tiered Storage Example

On other hand, storage for retaining large amounts of data that is in-active or idle for long periods of time should be looked at on a capacity per energy footprint basis. While final metrics are still being flushed out, some examples could be active storage gauged by IOPS or work or bandwidth per watt of energy per footprint while other storage for idle or inactive data could be looked at on a capacity per energy footprint basis.

What benchmarks or workloads to be used for simulating or measuring work or activity are still being discussed with proposals coming from various sources. For example SNIA GSI TWG are developing measurements and discussing metrics, as have the storage performance council (SPC) and SPEC among others including use of simulation tools such as IOmeter, VMware VMmark, TPC, Bonnie, or perhaps even Microsoft ESRP.

Tenants of Energy Star for Data Center Storage overtime hopefully will include:

Reflective of different types, categories, price-bands and storage usage scenarios
Measure storage efficiency for active work along with in-active or idle usage
Provide insight for both storage performance efficiency and effective capacity
Baseline or raw storage capacity along with effective enhanced optimized capacity
Easy to use metrics with more in-depth back ground or disclosure information

Ultimately the specification should help IT storage buyers and decision makers to compare and contrast different storage systems that are best suited and applicable to their usage scenarios.

This means measuring work or activity per energy footprint at a given capacity and data protection level to meet service requirements along with during in-active or idle periods. This also means showing storage that is capacity focused in terms of how much data can be stored in a given energy footprint.

One thing that will be tricky however will be differentiating GBytes per watt in terms of capacity, or, in terms of performance and bandwidth.

Here are some links to learn more:

EPA Energy Star for Data Center Storage Presentation
SNIA GSI Resources
Energy-Efficient Servers & Storage: The EPA’s Energy Star Ratings Are Coming
Chapter 5 (Measurement, Metrics, and Management) and Chapter 8 (Data Storage) in the book The Green and Virtual Data Center (CRC).
Chapter 10 (Performance and Capacity Planning): Resilient Storage Networks (Elsevier)
Green IT Efficiency and optimization calculators/tools
Green Storage is Alive and Well: ENERGY STAR Data Center Storage
Update: Energy Star for Server Workshop and more here
EPA Energy Star for Data Center Storage site
Twitter tag #ESStorage