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Going Green With Data Warehouse Appliances


Businesses are seeking ways to support green environments and sustainable energy programs. IT can make more significant contributions to such initiatives as vendors roll out energy program supporting hardware.

Recently, Terradata announced the combination of machines

Findings from a survey of IT professionals conducted by ONStor, a provider of NAS solutions, and reported by DM Direct in March 2008 included:

  • At their current data growth rate, 43 percent can only retain current performance in their current infrastructure for only 6 - 12 months
  • 24 percent reported that the most serious issue driving the reduction of data infrastructure power consumption is the cost and time of building another data center.


According to Gartner IT, the top strategic technology for 2008 is Green IT, based on the significant difference implementing Green IT will make in an organization’s work. Further, not doing so carries a competitive risk.

Green IT is impacting how IT groups budget and implement technology, based on challenging preconceived ideas around the physical attributes of the data center.

 

Green Solutions To Data

So how does a data center reconcile the challenging growth of data with shrinking physical resources.

Data Warehouse Appliances

One solution suggested is the data warehouse appliance [DWA].

The DWA combines processing and storage in one unit to address challenges of working with big data. It is economical in space requirements and extremely efficient in processing capacity. More on data warehouse appliances.

Power Efficiency

Hardware vendors now provide energy usage data for each of their products. For instance, Dell publishrd lab test results of their PowerEdge server for energy-conscious buyers to gain a detailed picture of power consumption.

Cooling Innovation

Cooling is one of the largest power users - HP’s Liquid Cooling technology, fans, venting and physical placement of components all reduce the thermal output or the power required to dissipate heat.

Physical Size

Less attention is paid to physical size and the impact it has on a machine’s greenness but size does matter when calculating the cost of floor space and new construction.

Together, ower consumption, thermal output and physical size establish a machine’s environmental footprint. Using density computations, the environmental cost of processing requirements can be quantified.

 

Computing Density

In a data warehousing environment, computing density is calculated per terabyte of data.

Typically, DWAs [by combining server and storage] offer energy efficiencies by:

  • Eliminating the redundancy that results from having servers dedicated to data processing and servers dedicated to data storage. There are generally fewer CPUs in play, fewer fans, etc.
  • Reducing load on network infrastructure as they process data near the point of storage. Moving data around the network uses large amounts of power. Communications equipment has the highest rate of energy consumption per square foot. Switches and fibre channel alone can account for 14 percent of a data center’s energy consumption.3 Some DWAs use fibre channel for inter-node or inter-appliance communication, however even they are conservative consumers compared to SAN storage devices.
  • Implementing Massively Parallel Processing (MPP) spreads work across CPUs and disks in such a way that lessens redundant spin or idle cycles.
    In addition to these efficiencies that are inherent in the appliance form factor, there are other techniques that vendors can use to further conserve energy, for example, selecting high-efficiency processors. AMD’s HE Opteron processors use 58 percent (95 watts cf. 55 watts for HE processors) of what their “standard” processors use. SATA drives and Flash memory, as well as more energy efficient RAID controllers and switches can contribute incrementally too.

Computing Density = (Power + Cooling) x Space

 

Cooling

Less Power Usage = less thermal ouput = less cooling = less power usage.


The cooling factor is an output of the power and space - it takes as much energy to cool a device as it does to power the device. Many energy consumption specs do not account for the cooling part of the equation.

Costs of cooling the data center include both internal equipment cooling as well as the cost of targeted ducts or vents.

In calculating the data center’s share of total resource usage, calculations must include both the power used by hardware and that used for ambient and supplemental cooling.

The table below illustrates the watt hours consumed by two physical data warehouse architectures under an average processing load: a host server for the DBMS with a storage server with 1.5TB capacity and a DWA with 2TB capacity.

Watt Hours Power Watts Cooling Watts
Total Watts
Server + SAN 1.5TB 24,900 25,500 50,400
DWA 2TB 300 300 600 + Host DB Server Usage

Watt Hours Comparison

The difference is significant and underscores the advantages that can be gained from using devices that have been engineered for specific tasks.



Footprint Space

Larger machines require more space; more space costs more money and has a larger ecological impact.

The efficient space usage of a DWA also contributes to a lower energy consumption; a single appliance as opposed to two pieces of independent hardware eliminates redundancy and is more compact.

The DWA offers both a smaller configuration as well as unique engineering to provide massive capacity within the smaller footprint. This is common to all DWAs.

Some DWAs combine third-party hardware preconfigured with specialized software. Whilst still very energy-efficient, the general general-purpose hardware used by these DWAs means their physical footprints end up similar to that of a nonappliance server-and-storage stack.

Comparison of Environmental Footprints and Data Capacity

Environmental Footprints 2 TB 10TB
DB Server + SAN Storage 2 ft3 20 ft3
DWA 1.13 9.9

For 2TB of data, a DWA’s environmental footprint is approximately 40 percent that of a server+SAN architecture. That ratio holds steady even as the data warehouse expands in volume.

Hardware [storage arrays, servers and networking equipment] accounts for 50 percent of the power requirements in the data center.

Energy-efficiency initiatives must focus at significantly reducing hardware. This is where virtualization technology is providing great savings. Energy efficiency and space requirements are now among the primary selection criteria for any new acquisition.

 

Teradata 5500 Server

Designed specifically for data warehousing, the high-availability Teradata 5500 server scales from hundreds of gigabytes to four petabytes. It provides the capability to support rapid expansion in data volumes, heavier decision-support demands and complex mixed workloads including deep analytics.

The dual-core Intel Xeon [5100] chip set is much more efficient at data warehouse workloads, compared to the Intel Itanium chip.

The Teradata 5500 server is a great example of improved carbon footprint over each of these three domains:

Power Usage - The Teradata 5500 Server uses 75 percent less energy for the same capability data warehouse compared to Teradata servers 3-5 years ago, according to Noel Pettitt, Teradata's general manager for Australia and New Zealand. That represents the typical power usage [kilowatt-hours] of 60 homes for a year!

Space - The 5500 Server requires approximately 66 percent less floor space.

Cooling - an improved cabinet design is said to increase cooling efficiency.


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