Data Center Infrastructure Management (DCIM) of IT Systems

PG&E’s Emerging Technologies Program ET11PGE1051

Data Center Infrastructure Management (DCIM)

of IT Systems

ET Project Number: ET11PGE1051

Project Manager: Ilyssa Lu Pacific Gas and Electric Company Prepared By: Jameson Morrell CH2M HILL 150 Spear Street, Suite 750 San Francisco, CA 94105

Issued: April 25, 2012

Copyright, 2012, Pacific Gas and Electric Company. All rights reserved.

i


ACKNOWLEDGEMENTS

Pacific Gas and Electric Company’s (PG&E’s) Emerging Technologies Program is responsible for this project. It was developed as part of PG&E’s Emerging Technology – DCIM program under internal project number ET11PGE1051. CH2M HILL team members Jameson Morrell, Chad Dikos, David Gray, and Andrew Solberg conducted this technology evaluation for PG&E with guidance and management from Ilyssa Lu. For more information on this project, contact [email protected].

LEGAL NOTICE

This report was prepared for PG&E for use by its employees and agents. Neither PG&E nor any of its employees and agents:

(1) Makes any written or oral warranty, expressed or implied, including but not limited to those concerning merchantability or fitness for a particular purpose;

(2) Assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or

(3) Represents that its use would not infringe any privately owned rights, including but not limited

to patents, trademarks, or copyrights.

ii


ABBREVIATIONS AND ACRONYMS

BMS building management system

CMBD configuration management database

ECM energy conservation measure

DCIM data center infrastructure management

IT information technology

kW Kilowatt

kWh kilowatt-hour

PG&E Pacific Gas and Electric Company

PUE power utilization effectiveness

ROI return on investment

SLA service level agreement

UPS uninterruptable power supply

VM virtual machine

iii


CONTENTS

EXECUTIVE SUMMARY ____________________________________________________ 1

INTRODUCTION _________________________________________________________ 2

BACKGROUND __________________________________________________________ 2

EMERGING TECHNOLOGY _________________________________________________ 4

ASSESSMENT OBJECTIVES __________________________________________________ 6

Goals of Sentilla Management .................................................. 6

TECHNOLOGY EVALUATION ________________________________________________ 7

APPROACH ____________________________________________________________ 8

RESULTS _______________________________________________________________ 9

Installation and Mapping ......................................................... 9

Outputs and Analysis ............................................................ 10

Output Validation ................................................................. 10

EVALUATIONS _________________________________________________________ 11

Software Implementation and Outputs .................................... 11

OBSERVED SAVINGS AND BENEFITS _________________________________________ 12

Qualitative .......................................................................... 12

Quantitative ........................................................................ 12

Alignment with Initial expectations ......................................... 12

Sentilla post-installation review 13 AT&T post-instillation review 13

RECOMMENDATIONS ____________________________________________________ 14

“Dense” data centers with high asset utilization ....................... 14

Market Adoption ................................................................... 14

PG&E Incentive .................................................................... 15

APPENDICES __________________________________________________________ 16

Appendix A – Business Case and Incentive Calculator ............... 16

Business case simulation (Sheet 1) 16 Incentive Calculation (Sheet 2) 17

Appendix B – DCIM Vendors .................................................. 18

DCIM vendors identified: 18 Viridity: (http://www.viridity.com) 18

iv


Nlyte: (http://www.nlyte.com) 19

Synapsense: (http://www.synapsense.com) 19

Sentilla: (http://www.sentilla.com) 20

REFERENCES ___________________________________________________________ 21

1


EXECUTIVE SUMMARY

PROJECT GOAL

While there has long been building management systems software to aid management with

decisions about energy efficiency, the software to measure the energy efficiency of IT

systems in data centers has only recently been developed. This new type of software is the

focus of this emerging technology assessment.

PROJECT DESCRIPTION

PG&E arranged for CH2M HILL to observe and validate a proof of concept for the

installation, data management, analysis, outputs, and recommendations of a software

provider, Sentilla Software, for a large data center operator, AT&T. Sentilla’s software fills a

new niche not previously monitored or visible to data center managers. The Sentilla product

loads on top of existing systems and “manages the managers” who collect information.

CH2M HILL functionally assessed the main software, which included functions for monitoring

and calculating energy usage and server utilization. The Sentilla software ultimately

provides a visual map of the energy usage and percentage of utilization across a defined

boundary in the data center. Energy and usage charts combined with engineering and data

center experience help determine which servers could be consolidated or shut down (for

example, identifying servers with constant power draws over period of time as an indicator

that there is little to no server activity).

PROJECT RESULTS

Overall, there were positive sentiments regarding the fact that Sentilla’s software met the

goals of identifying equipment that could be consolidated or taken offline and demonstrating

potential savings for AT&T.

Both software supplier and data center operators were pleased with the initial investment,

installation, outputs, and findings from the implementation of Sentilla software. Accepting

the proof of concept and rolling the software out to full production sites was the ultimate

positive outcome of this technology assessment.

PROJECT RECOMMENDATIONS

The Sentilla software was easy to deploy, nonintrusive, and produced data outputs that

converted into easily understood reports regarding idle and underutilized servers. With

other software suppliers entering this marketplace, companies will soon have multiple

options to cost-effectively and quickly monitor server utilization. This new generation of

software will be largely useful in three ways:

1. Information used to build a business case for mobilizing the process and governance

to regularly consolidate or decommission servers

2. The ongoing monitoring of IT equipment for regular preventative maintenance and

maintaining efficiency performance standards

3. Consolidating racks and servers will save space and allow for a greater density of

servers and less data centers that may free up capital for other uses.

2


INTRODUCTION While the growth in data center electricity demand has slowed, it currently accounts for

2 percent of all electricity used in the United States (Koomey, 2011). Data centers are

generally large facilities, in a single location, with concentrated electricity demands.

Historically, due both to the concentration and to large amounts of energy required by data

centers, innovative research and development has established a focus for energy

conservation. However, efforts to increase the energy efficiency of data centers have

primarily focused on electrical usage in facilities, for example, on cooling, air handling, and

power loss during distribution.

Currently, power utilization effectiveness (PUE) is the metric used to benchmark the energy

efficiency of data centers. The PUE is derived by dividing the total power used by the

amount of power used for information technology (IT) equipment:

PUE = Total data center power use

IT equipment power use

The average PUE for data centers is a ratio between 1.8 and 1.92 (Koomey, 2011), down

from 2.0 estimated in 2008. (Koomey, 2008) Most of the improvement in PUE can be

credited to the improved efficiency of data center design. More efficient facility designs and

retrofits have lowered the non-IT equipment power usage, the numerator in the PUE

equation. However, the denominator IT equipment power use has remained constant (for

example, 20/10 = 2; 18/10 = 1.8 over the last few years.

The focus of a new trend in energy efficiency and this emerging technology assessment is

the PUE denominator: IT technology equipment power use. Added efficiency can

achieved by installing more efficient individual hardware components such as Energy Star-

rated servers and by exercising operational discipline in turning off or consolidating

underutilized IT equipment. Common sense dictates that both the efficiency and usage of

facilities and IT equipment be addressed. While there has long been building management

systems software to aid management with decisions about energy efficiency, the software to

measure the energy efficiency of IT systems in data centers has only recently been

developed. This new type of software is the focus of this emerging technology assessment.

BACKGROUND There is an emerging market for energy efficiency focused exclusively on reducing IT

equipment power use, the denominator of PUE. And while IT technology hardware

equipment manufacturers have recently made progress in energy efficiency, the impacts of

efficient server designs and data storage configurations and the growth in cloud computing

are burgeoning and hard to ascertain (McKinsey, 2010).

Meanwhile, two inefficiencies in data centers stand out: powering and maintaining unused

and underutilized servers. A conservative estimate, based on data center surveys and

studies, places the level of unused (“comatose” or “zombie”) servers in data centers at

3


about 10 percent (Green Grid, 2010). This means that large data centers with 5,000

servers, for example, house about 500 idle servers. These servers are fully powered but

idle, on hold to accommodate computing demand that never comes. How does this happen?

Root-cause analysis indicates that IT solutions are over-engineered and overbuilt because

business units often procure IT services in “silos” based on initial reliability and sizing

estimates. In other words, a best guess estimate is made without actually knowing the

actual computing needs and this leads to over engineered solutions to account for

overlapping worst-case scenarios. It is standard practice for business units to engage IT

support to design, procure, build, and operate IT solutions. There is generally a 1:1

relationship between IT and business unit with a service level agreement (SLA) approved for

the ongoing operation of each server.

This standard practice has led to the development of many silos of business IT servers

operating in isolation, with excessive reserves of computing power. These silo-ed servers

are idle or underutilized, stacked next to one another on racks, creating “server sprawl” in

data centers. Studies show estimates of server utilization rates can be as low as 7 to 10

percent. For every 100 watts of power consumed by data centers, only 3 watts are

associated with computing (Aggar, 2011). If the denominator in PUE was changed to the

“computing” power of IT equipment, these data centers would score a ratio of 33 instead of

approximately 2.

CH2M HILL estimates that the cost for energy, operations and maintenance, and capital

investment to operate an average server is roughly $4,900 a year. For large data centers,

this may add up to millions of dollars a year spent on idle servers. That estimate accounts

for the cost of zombies alone and does not even include the potential cost savings to be

realized by consolidating underutilized hardware (Simulation tool provided in Appendix A).

The reason IT and business managers have not addressed this issue is visibility: there is a

lack of mapping to identify underutilized IT hardware in data centers.

4


EMERGING TECHNOLOGY Most IT managers do not have a detailed view of IT equipment energy consumption because

equipment supports operational functions with widely varying demand patterns. Also,

energy use has traditionally not been tracked. The decision to consolidate or remove idle

equipment is made on a case-by-case basis with limited visibility of entire data center

consumption patterns. Further, many business managers are not aware of the lifetime costs

associated with idle servers or underutilized IT equipment. They are, instead, focused

narrowly on annual budgets for IT-related SLAs.

To fill this visibility void, IT equipment manufacturers and software designers are beginning

to develop equipment and software that generate reports and visualizations of actual or

computed utilization metrics across disparate IT equipment in data centers. As a result, data

center managers may now install software that will provide regular views across IT

equipment to spot idle or underutilized outliers. Both IT and business managers may use

this data to develop business cases and processes to turn off and consolidate IT equipment,

thus saving energy, operation and maintenance costs, and capital earmarked for new

data centers.

Ideally, a data center infrastructure management (DCIM) information solution will provide

visibility across all three data center assets and operations, including:

1. Facility systems (cooling and power)

2. Network systems (cabling, area networks)

3. IT systems (servers, storage, applications, & virtual machine - VM)

CH2M HILL conducted a limited review of ten software tools in this market niche. Four of

those tools were reviewed using a more detailed “desktop analysis” based on available

public information (Appendix B). The suppliers of software solutions currently available all

claim to meet the ideal standard, but programs tend to exhibit capabilities focused on one

type of data center system. All suppliers claim that their software will increase the visibility

and quantification of historical and real-time data in order to optimize existing systems and

provide accurate information for future system optimization and planning. The premise is:

more efficient facility and IT systems lead to cost savings.

A complete DCIM system solution addresses all three data center systems with regard to

these subjects:

Asset tracking (all three systems)

Real-time data collection

Historical data collection

Performance visualization

Capacity planning and reporting

Single data repository

Holistic approach

5


As discussed earlier, data center energy use falls into three general categories: facility

systems, network systems, and IT systems. The figures below show a typical energy

allocation among these systems.

6


The range of software programs currently available in the market place all seem to fall short

of providing a complete DCIM solution. Current software tools seem to focus expertise on a

specific data center system, but have gaps that do not support a holistic solution. For

example, one solution seems to address IT systems well, but lacks details on facility

systems. Another system presents the opposite problem: the product is robust for

measuring data center temperatures and monitoring facility systems, but weak for

measuring IT systems.

As DCIM solutions mature, the vendors with the most holistic approach will offer the data

center business the greatest value. It is therefore important to identify and understand the

gaps in software products. Existing solutions are generally strong in measuring facility

systems and there is a new emerging market for measuring the efficiency of IT systems. In

the future, these two specialty areas will likely merge into a single, well-integrated holistic

solution where, for example, computing demand triggers a cooling demand: a “just-in-time”

approach to energy use. This development would follow other trends in energy efficiency

that, in essence, automatically turns off energy supplies when demand lowers.

ASSESSMENT OBJECTIVES PG&E arranged for CH2M HILL to observe and validate a proof of concept for the

installation, data management, analysis, outputs, and recommendations of a software

provider, Sentilla Software, for a large data center operator, AT&T.

The software itself does not reduce energy usage; it is merely the means to an end. To

realize the benefits of the visibility provided by the software, a process and governance

must first be implemented. Ultimately, CH2M HILL’s evaluation rests on the assumption that

the software will meet the expectations of both the software provider and data center

operator. To set the baseline for success for the proof of concept, CH2M HILL interviewed

stakeholders from Sentilla Software and AT&T.

GOALS OF SENTILLA MANAGEMENT Conduct a technology “proof of concept” of an IT system-tracking tool that

can improve data center “hardware” efficiency and reduce energy demand

and costs.

Focus tracking specifically on optimizing the IT load of a data center to use

less energy, and not on lighting; heating, ventilation, and air conditioning,

and so forth (Because these are all part of a connected system, a decrease in

IT loads will have an impact on other aspects and reduce energy demand).

Demonstrate the software’s effectiveness to improve energy effectiveness.

Prove the concept of Sentilla software at an AT&T data center.

Identify dormant, broken, and inefficient equipment to consolidate or take it

offline and demonstrate potential for savings for large companies such as

AT&T, who have been running applications in multiple data centers for

decades.

Expect to demonstrate the potential for an approximate 15-percent reduction

in energy costs.

7


Goals of AT&T MANAGEMENT

AT&T has many large data centers and, therefore, is making a significant

investment to achieve large energy and cost savings. The pilot proof of

concept aims to validate and achieve a fast return on investment (ROI).

Sentilla Software will provide AT&T with intelligence regarding the electrical

distribution load (uninterruptible power supply [UPS] and power distribution

unit [PDU]) and capacity level. When adding servers, the goal is to add them

to a floor that is running at 30-percent capacity and not at 80-percent

capacity. This will provide for maximum efficiency of power distribution (and

identify where to add load and save incremental bits of energy. Note that this

will not be the big energy saver).

Sentilla software can “reach out and touch” each appliance to determine and

monitor server capacity (CPU usage). Visibility will enable AT&T to merge

applications from several servers on one server while continuing to monitor

and document how much power servers use. Combining CPU usage and

power monitoring can help IT engineers make better decisions. This will

initiate a “domino” effect that will allow applications to be re-racked and

stacked within a smaller footprint. This will result in more space, more racks,

and better growth with more efficient use of energy.

The objective of this technology assessment is to observe the deployment of

an IT systems-focused solution with a large commercial operator of data

centers.

CH2M HILL is bringing together expertise in two areas to deliver this assessment:

the design, construction, and operation of data centers and the deployment of

software solutions. Together, these two technical perspectives cover the full range of

opportunities for energy efficiency in facilities and operations.

TECHNOLOGY EVALUATION Sentilla’s software fills a new niche not previously monitored or visible to data center

managers. The Sentilla product is “nonintrusive;” it loads on top of existing systems and

“manages the managers” who collect information. Typical software users include: data

center manager, building managers, and IT services department staff, specifically managers

of various SLAs. The software can generate a report that provides a snapshot of a defined

time that can, in turn, be used to brief business managers who generally do not log on to

the system as end users.

CH2M HILL functionally assessed the main software, which included functions for

monitoring and calculating energy usage directly, where available, or as a function of the

percentage of utilization and manufacturing specifications. The software works in several

ways by treating the data center racks and servers as variables in an equation where the

software calculates the total consumption level. Variables for different racks of servers are

calculated directly by monitoring energy usage and the percentage of utilization. For other

servers, the percentage of utilization telemetry data is known and manufacturing

specifications are used to estimate energy usage. In other cases, energy is monitored at a

8


higher (rack) level and each server’s energy usage is estimated using a back calculation

based on manufacturing specifications. The Sentilla software ultimately provides a visual

map of the energy usage and percentage of utilization across a defined boundary in the data

center. Sentilla Software also offers other modeling and predictive modules that were not

observed during this assessment.

APPROACH CH2M HILL staff researched the DCIM software market and attended introductory and

training sessions with Sentilla staff while arranging to observe and, in some cases,

participate, in several steps, including:

Onsite installation of Sentilla software

Onsite mapping and data management exercises

Review of analysis and outputs with Sentilla and AT&T representatives

Attendance at meetings between Sentilla and AT&T representatives reviewing

recommendations on idle and underutilized equipment

Meeting with AT&T representatives to validate data outputs and observations

Before and after each step, CH2M HILL staff interviewed Sentilla and AT&T representatives

to assess progress toward goals outlined earlier in this report (see: Assessment Objectives).

The critical process that CH2M HILL staff assessed was the reaction of AT&T to the server

utilization and energy usage made visible via Sentilla reports.

The AT&T data center for this implementation was a development/testing site where a

heterogeneous mix of hardware supports numerous testing efforts for AT&T business units.

The approach rationale was to test Sentilla software in a development environment both to

prove the software capabilities and check for any impacts on the operation of data center

systems. (AT&T production facilities have more “homogenous” hardware and applications.)

Because the data center location is a development environment, a few key observations

need to be noted. There is greater mix of old and new equipment technologies in

development facilities than in production data centers. A higher level of asset changeover in

development environments makes it harder, in some cases, to track and test Sentilla

software capabilities.

The fact that it was a development environment made it difficult to track down the owners

of servers for permission to deploy the Sentilla software. This actually delayed the rollout

and led to the use of a smaller data set of servers. This later proved to be an issue when

attempting to quickly turn around and act on the recommendation reports generated by

the software (discussed in more detail in the results and evaluation sections that follow).

9


RESULTS

INSTALLATION AND MAPPING The following is overview of the results observed by CH2M HILL personnel who

shadowed Sentilla staff both onsite at the AT&T data center and during multiple

offsite meetings. CH2M HILL staff observed the installation of Sentilla software and

the onsite mapping of and integration of IT equipment with various data sources in

order to calculate energy consumption and utilization percentages.

Initially, Sentilla staff installed the Sentilla software application on one server onsite

at the data center. For security, the system runs onsite on AT&T’s network, ensuring

all data and analysis remains inside AT&T’s network with no outside connections.

A significant effort was made to get security permissions to access every server. This

delayed the project timeline significantly and ultimately led to having to use a

smaller sample data set of servers for the proof of concept trial. This is likely only an

issue at test/development data centers; AT&T representatives indicated that

production data centers have clearer server and account definitions and a single

permission covers a broader swath of hardware.

For software configuration, data centers typically have an asset management

database that defines the server hierarchy. There was some effort involved in

integrating Sentilla software with different systems to get the data. At the

test/development data center, the UPS comes from a building management system

(BMS) system. The “meter level” data comes from a configuration management

database (CMDB) system.

Data Center Impact: There appeared to be minimal impact on data center staff or

systems during implementation for data configuration. A Sentilla programmer can

complete the entire data mapping by herself/himself, provided there is a sufficient

asset management system that defines the assets and a readable hierarchy

is available.

A Sentilla programmer toured the facility and inventoried the floor (all racks and

servers) to verify that what was mapped in the software conformed with the

hardware the Sentilla software was monitoring.

Once mapped, software was configured by Sentilla staff and test reports run. Test

reports consisted of data, exported into Microsoft (MS) Excel, that were converted

into usable graphics and inserted into MS PowerPoint slides, which were used when

briefing AT&T representatives on asset utilization and overall efficiency.

The elapsed time for installation was a matter of months. This was primarily due to

the delays in securing permission from various business owners to map servers

onsite. The time from server installation of Sentilla software to initial data input was

a matter of days. Initially the system was to have been run over a period of 90

days, with outputs reviewed at 30-day intervals to ascertain a behavior of time

trend. However, the delays due to the number of permissions required shortened the

proof of concept phase to a single 30-day period.

10


OUTPUTS AND ANALYSIS Sentilla representatives met with AT&T representatives to review the outputs and

analysis and to agree on the next steps for following the proof of concept. In

addition, Sentilla and CH2M HILL representatives reviewed both the outputs and

analysis. CH2M HILL secured server identification numbers to validate independently

with AT&T that the servers onsite were the ones that had been monitored and that

the results captured in the Sentilla system reflected the characteristics of those

servers.

Together, CH2M HILL and Sentilla staff reviewed a spreadsheet and calculations,

provided by Sentilla, that reflected an initial 30-day data collection period. A

presentation between AT&T and Sentilla representatives contained the same

information proof of concept results presented included:

149 systems were monitored

16 idle servers were found

56 servers had <10-percent utilization

Total of 72 servers were idle/underutilized

Estimates by Sentilla for taking servers offline:

11,572 estimated kilowatt-hours saved for the June 16 to July 13 period

$25,000 estimated energy savings per year

$18,000 estimated savings in basic server contract costs per year

An additional 60-day monitoring period confirmed that the servers were, indeed,

idle/underutilized and a decommission recommendation report was prepared by

Sentilla staff for AT&T representatives.

OUTPUT VALIDATION CH2M HILL, Sentilla Software, PG&E, and AT&T staff met and reviewed the analysis

and discussed next steps. Discussion focused on the 16 idle servers. AT&T staff

confirmed that they had seen 16 idle servers and had escalated this to the Windows

asset team (the representing staff was non-Windows).The ensuing conversation

outlined the challenges to simply “pulling the plug” on the idle servers, including:

Process and authorization steps do not exist to turn off idle servers

Separation of teams by server type complicated decision-making authority

Servers were leased and a separate lease team needed to be involved, which

further diluted decision-making authority

There was concern that the 30-day testing window was too short to secure a

data sample for a test/development environment (this would not be an issue

for production centers)

AT&T managers agreed that a decommissioning process needed to be defined quickly

and that there were clear cost and energy savings that would interest AT&T

Operations and Green Teams.

11


Overall, AT&T managers were impressed with the visibility provided by the Sentilla

software, and deemed it a huge step forward from the current process. The current

process involves teams manually roving data centers to search for underutilized

assets. Making underutilized assets more “visible” would be a massive advantage.

AT&T managers reached agreement to deploy Sentilla software at a full production

data center.

Communication efforts to follow up with AT&T on the decommissioning of the 16 idle

servers were not completed satisfactorily because the period for this technology

assessment expired before an action could be reported to CH2M HILL reviewers.

EVALUATIONS

SOFTWARE IMPLEMENTATION AND OUTPUTS Relatively speaking, the Sentilla software appears to be a nonintrusive, easy-to-

install application that quickly delivers visibility across data center IT equipment. The

actual recommendation reporting on underutilized and idle servers appears to be a

semi-manual process. The software, as observed by CH2M HILL staff, did not

produce a report that says “x, y, and z servers are zombies.” The software did

provide the end user with real-time energy and usage charts that could be

downloaded and combined with engineering and data center experience to determine

which servers could be shut down (for example, identifying servers with constant

power draws over period of time as an indicator that there is little to no

server activity).

Aside from server access, the software requires little effort from data center staff to

implement. The significant effort observed to get security/server access is likely due

to the fact the data center is a test/development environment. AT&T staff assumed

that production data centers would have clearer ownership, which would make it

easier to identify business owners and quickly gain permission to deploy.

Ongoing maintenance to update the Sentilla software could take the form of using a

“batch” process wherein utilization reports would periodically aid data center staff to

clean out underutilized servers. Another option would be to have the Sentilla

software continuously synchronize with the CMDB, which would enable the system to

remain current without input from an operator. The Sentilla software automatically

alerts data center staff when a new device appears in the CMDB.

Software updates can be made remotely or onsite, depending on the level of access

available to Sentilla staff. For an AT&T data center, server and data sensitivity might

preclude access over the internet and, in that case, an administrator would most

likely need to be at the data center to view or run reports and updates.

12


OBSERVED SAVINGS AND BENEFITS

QUALITATIVE AT&T reported that having an “actionable” report of underutilized servers would

provide a map for energy savings. Without this type of report, technicians would

need to hunt and peck for servers to unplug, and even then, would be unsure of

whether a server was critical or in use. That approach would not be helpful in gaining

traction for the effort to unplug unused servers.

The ease with which Sentilla software identifies underutilized servers would create

the momentum for data centers and business mangers to develop processes,

objectives, and metrics to consolidate servers, reduce energy usage, and free capital

earmarked for additional data centers and IT equipment. Actionable reports would be

catalysts for organizational change. Data center managers would find it difficult to

justify why facilities still have idle and underutilized servers in the face of a report

that says, in very basic terms, “unplug these servers.”

QUANTITATIVE Business case simulations (Appendix A, Sheet A) reveal the types of potential

benefits that AT&T might realize by monitoring at the CPU level, using a friendly

reporting interface to identify idle or underutilized equipment:

When removing a server, from an energy standpoint, there is a two-fold

impact: it reduces baseline energy usage by more watts than the server uses

due to PUE and distribution losses.

Each server has associated annual license, maintenance, and software fees

that are accounted for in the SLA to cover “run costs.”

Understanding utilization rates across processors in real-time, 24 hours a day,

encourages consolidation, which increases the density of IT equipment by

using many different consolidation techniques (VMware, cloud, and so forth).

However, the first step is to visualize the opportunity. Ongoing monitoring

encourages moving away from “server sprawl” to creating dense, efficient

data centers.

The ability to compare a “healthy” server with equipment with signs of

breaking down leads to more effective scheduling of maintenance and cost-

saving warranty work.

Dense, efficient data centers free up capital normally reserved to pay for

more and more IT equipment and data centers. What started as an efficiency

project actually has the potential to become a major capital savings initiative

for companies who spend a lot on IT services.

ALIGNMENT WITH INITIAL EXPECTATIONS A traditional technology assessment is generally a head-to-head comparison of some

type of new technology to legacy devices. However, this technology assessment

13


differs in that the software reviewed provides visibility into the operational

performance of equipment in much the same way as metering devices or other

energy auditing assessment tools, which, in turn, lead to energy conservation

measures (ECMs) recommendations. In that scenario, an evaluation of the actual

performance of the software against the initial expectations of end users is critical.

Did the software perform as expected?

SENTILLA POST-INSTALLATION REVIEW

CH2M HILL team members met and debriefed with Sentilla representatives on the

lessons and observations of the installation at the AT&T data center. Below are key

findings from these meetings.

Getting access to the servers took longer than originally anticipated (delays

from original project plan). The consensus was that it was a little more

difficult than usual, potentially due to the testing and development nature of

the target data center.

Sentilla Software representatives wanted to incorporate more servers into

monitoring, but were nonetheless pleased to find the idle or

underutilized servers.

Based on the Sentilla Software recommendation report to the AT&T

decommissioning team, Sentilla technicians were confident that the server

information and data provided to the decommission team was valid.

AT&T representatives suggested that the proof of concept results may

actually be three times higher in a production environment than in the testing

facility used for this assessment.

Sentilla staff were excited that the software passed the proof of concept and

are beginning work to roll out software to AT&T production data centers.

Overall, there were positive sentiments regarding the fact that Sentilla’s

software met the goals of identifying equipment that could be consolidated or

taken offline and demonstrating potential savings for AT&T.

AT&T POST-INSTILLATION REVIEW

These key points emerged during post-installation meetings between CH2M HILL

team members and AT&T management:

AT&T has faith that Sentilla software deploys in a nonintrusive way and

delivers valuable visibility into idle or underutilized assets.

Sentilla software output motivated AT&T managers to take immediate action

to look at decommissioning or consolidating underutilized assets.

AT&T managers gained insight into internal barriers to discovering idle

servers, but found that internal organization and processes initially

hampered action.

AT&T managers moved immediately to deploy Sentilla software into

production sites beyond the proof of concept and to develop a concrete

business case for wider adoption.

14


Both software supplier and data center operators were pleased with the initial

investment, installation, outputs, and findings from the implementation of Sentilla

software. Accepting the proof of concept and rolling the software out to full

production sites was the ultimate positive outcome of this technology assessment.

RECOMMENDATIONS

“DENSE” DATA CENTERS WITH HIGH ASSET UTILIZATION The Sentilla software was easy to deploy, nonintrusive, and produced data outputs

that converted into easily understood reports regarding idle and underutilized

servers. With other software suppliers entering this marketplace, companies will

soon have multiple options to cost-effectively and quickly monitor server utilization.

This new generation of software will be largely useful in three ways:

1. It can be used to build a business case for mobilizing the process and

governance to consolidate or decommission servers based on potential cost

savings. Business and IT managers working together, with clean metrics, can

reap solid savings on hardware costs, license fees, and energy costs by using

this software to manage results and document cost and energy savings.

Savings may be realized within months of deployment, depending on the

organizational ability to quickly assess and act on opportunities.

2. The ongoing monitoring of IT equipment performance for preventative

maintenance and efficiency may encourage the development of contractual

agreements with hardware suppliers to guarantee performance. Monitoring IT

components for underperformance on efficiency specifications will enable

quick servicing or replacement of equipment based on contractual service

levels. Over time, this will hold suppliers accountable for efficiency claims and

ultimately conserve additional energy and run costs.

3. Finally, consolidating racks and servers will save space and allow for a greater

density of efficiently utilized and maintained servers. Given the typical

computing needs of today’s companies, the need for less space will translate

into fewer data centers, which will lower costs and free up capital for other

uses, for example, to upgrade servers to more efficient models.

All three of these processes are dependent upon a trend line that demonstrates, over

time, the actual versus expected performance and utilization of IT assets. That

information by itself will not generate cost savings. To realize the cost savings

benefits, organizations must interpret and act on the data by establishing clear

processes and governance. Thus, the software is the means to end; it is critical for

the ongoing measurement and verification of any efficiency program.

MARKET ADOPTION Overall, there is a strong business case (see Appendix A) for data center managers

to adopt software that can identify idle and underutilized equipment in order to

consolidate or decommission it. The payback is quick and tangible. Beyond the

15


financial benefits, there is currently a market and government focus on the high

amount of energy consumption in data centers. There is call for greater efficiency

and accountability in these facilities. Further, “watch dog” organizations are publicly

pushing for greater transparency regarding the energy and carbon footprints of

corporate data centers and calling for greater investment in renewable energy. Even

if data centers shift to renewable energy, poor server utilization rates would imply

that the energy is, in essence, being wasted. Renewable energy used to power idle

or underutilized IT assets would be drawn from a limited resource pool and would no

longer be available to offset the use of nonrenewable energy, thereby creating

unnecessary energy production. When social, political, and economic forces start to

align, it is only a matter of time before server utilization joins PUE as a key

benchmark. This makes a strong case for further expansion and adoption of DCIM

solutions that monitor IT systems.

PG&E INCENTIVE Even though DCIM-specific software to monitor IT systems is an emerging

technology, it does provide the data required to identify energy conservation

measures in much the same way that a facility energy audit identifies conservation

measures and payback scenarios. An organization still must take measures to

achieve energy savings and there is generally an incentive for embracing the

technology that replaces older, less efficient assets. Incentives entice organizations

to act on new technology and, in some cases, help adjust the ROI to fit payback

periods within capital expenditure guidelines and jump-start adoption.

It is unlikely that there will be an incentive for software that does not deliver energy

conservation directly. A more traditional approach would be to incentivize and speed

the replacement of older, less efficient servers while consolidating and increasing the

density and utilization of servers in data centers. In that case, an incentive could be

awarded for each idle server decommissioned (Appendix A, Sheet B). As a pre-

requisite, software could be deployed to identify idle or underutilized servers within a

given boundary of racks and to monitor and verify that server population utilization

rates increase.

These types of incentives mirror the more typical asset-for-asset replacement-type

incentives normally deployed by PG&E. Additionally the software performs an audit-

like function by defining a boundary and providing auditable “before” and “after”

snapshots. The incentive achieves several objectives by rewarding:

Investment in visibility software

Decommissioning of servers

Reporting and managing the utilization rate as a metric

The recommendation is to publicize and reward active management of idle and

underutilized servers. There is no need for an incentive to improve the business case

to turn off or consolidate servers. Operations and maintenance and capital savings

alone would entice any business manager with visibility data on a large number of

idle or underutilized servers.

16


APPENDICES

APPENDIX A – BUSINESS CASE AND INCENTIVE CALCULATOR

Server Consolidation Simulation PGE 1-5-12 D.xlsx

BUSINESS CASE SIMULATION (SHEET 1)

Sheet A contains a business case simulator allowing an organization to input

variables to simulate benefits such as energy and operational savings as well net

present value and internal rate of return. Input variables include:

Name of Data Center

Location

Contact

Phone Number

Existing Data Center Conditions

Number of Energized Racks in Data Center

Average Servers per Rack

Average Peak Power Draw per Active Server (watts)

Typical Server Load Factor

Average Idle Server Power Draw as a % of an Active Server

Marginal Power Usage Effectiveness (PUE)

Average Electric Rate ($ per kilowatt-hour [kWh])

Marginal Annual O&M per Server ($)

Capital Cost per Server in Data Center ($)

Cost of Capital

Changes from Implementation of Recommendations

Reduction in Energized Server Requirements (%)

Percent of Surplus Servers Decommissioned

Year that Capital Savings Are Realized

Monetization of Increased Risk, if Any (Annual Cost)

Monetization of Reduced Risk, if Any (Annual Benefit)

Annual Software License Fee ($)

Incentives by PG&E

17


Payment per Annual kWh Saved

Payment per kilowatt (kW) of Reduced Demand

Implementation Costs ($)

Life of Software License (input 1 to 10 years)

INCENTIVE CALCULATION (SHEET 2)

Incentive calculation can be determined by setting an incentive value in the business

case simulation (Line 28, Column D) for payment per annual kWh saved and/or

payment per kilowatt of reduced demand. The incentive calculator in Sheet 2 will

compute an incentive, depending on the scenario presented in the simulation, to

provide an amount per server.

18


APPENDIX B – DCIM VENDORS

DCIM VENDORS IDENTIFIED:

1. Nlyte http://www.nlyte.com/

2. Sentilla http://www.sentilla.com/

3. Viridity http://www.viridity.com/

4. Power Assure http://www.powerassure.com/products/data-center-

optimization-products

5. SynapSense http://www.synapsense.com/go/index.cfm

6. 1E http://www.1e.com/

7. Miserware http://www.miserware.com/company/

8. EnergyWare http://www.energyware.com/

9. Modius, http://www.modius.com/

10. OSIsoft

http://www.osisoft.com/value/industry/Datacenter_IT___Telecom.aspx

11. Aperature (Emerson)

http://emersonnetworkpower.com/enUS/Brands/Aperture/Pages/default.aspx

12. Fieldview Solutions http://www.fieldviewsolutions.com/

VIRIDITY: (HTTP://WWW.VIRIDITY.COM)

Products/Solution Brands - Energy Center, Energy Check

Information at the component level (device, rack, row, and at the data center levels)

for a comprehensive understanding of how a device is configured and how much

power the device is using in total.

Unobtrusive software-only approach.

Integrates with VMware‘s vSphere software, enabling administrators to examine the

utilization and power use of virtual machines.

Ability for users to group IT assets by owner or by the application they support. For

example, a user can group and examine all servers that support a transaction-

processing application; this might help the user facilitate an internal chargeback for

IT services to the appropriate line of business.

Datacenter Genome Database (GxDB), the repository of IT equipment power and

utilization information that Viridity uses to estimate IT power draw from measured

utilization values.

Software-only, it can have a lower price point and faster deployment time than

systems that come bundled with sensors.

19


DCIM solution:

Asset Tracking ………………... No (Facility Systems), Yes (IT Systems)

Real Time Data Collection…… Yes

Historical Data Collection…….. Yes

Performance Visualization…… Yes

Capacity Planning…………….. No

Reporting ………………………. Yes

Single Data Repository……….. No

Holistic Approach……………… No

NLYTE: (HTTP://WWW.NLYTE.COM)

Products/Solution Brands - nlyte 6.0

Nlyte Process: Discover, Visualize, Model effects of change, Control, Report, Predict

Uses sophisticated automation to optimize the physical capacities of power, cooling,

and space while improving IT service delivery

Good visualization tools – dashboard, data center room layout cabinet loading

PUE calculations

Does not appear to have temperature, airflow, and pressure monitoring

DCIM solution:

Asset Tracking ………………... No (Facility Systems), Yes (IT Systems)




Capacity Planning…………….. Yes (IT systems)


Single Data Repository ……….. No

Holistic Approach……………… No

SYNAPSENSE: (HTTP://WWW.SYNAPSENSE.COM/GO/INDEX.CFM)

Products/Solution Brands – Environmental Monitoring, Real-Time PUE,

SynapSense Active Control, Assessment & Optimization Consulting Services,

Power Suite, LiveImaging

Wireless real-time and historical monitoring of data center temperature and pressure

Graphical visualization of data, “LiveImaging”

Wireless P3 Smart Plug and SmartLink sensors for measuring average and

peak power

20


Branch Circuit Monitoring (BCM) supporting up to 42 panel board

Overall, appears to be focused on facility systems and not IT systems or

network systems

Not apparent that their solutions are integrated into a single, holistic approach

DCIM solution:

Asset Tracking ………………... Yes (Facility Systems), No (IT Systems)




Capacity Planning…………….. Yes, (Facility Systems), No (IT Systems)


Single Data Repository ………. No

Holistic Approach………………. No

SENTILLA: (HTTP://WWW.SENTILLA.COM)

Products/Solution Brands – Sentilla Integration Software delivered to customers

through both Software-as-a-Services (SaaS) on demand as well as on premise with a

physical or virtual appliance.

Sentilla integrates information from IT equipment and facilities, providing a

comprehensive, single view of the entire data center’s profile. Monitored data types

include system utilization and availability, network traffic, energy consumption,

temperature, and more.

Monitors utilization efficiency of your IT infrastructure. Sentilla tracks IT utilization,

costs, and efficiency of systems and applications in one place.

Overall, seems to be dependent on existing system monitoring systems to be able to

access information. Holistic solution if coupled with complete monitoring of

facility systems.

DCIM solution:

Asset Tracking ………………... Yes (Facility Systems), Yes (IT Systems)

Real Time Data Collection…… Yes (depending on existing

instrumentation)



Capacity Planning…………….. Yes, (Facility Systems), Yes (IT Systems)


Single Data Repository ……….. Yes

Holistic Approach……………… Yes

21


REFERENCES Andy Hawkins (2011), Determining the Implications of Unused Servers and How They

Can Be Addressed. Presentation: The Green Grid Technical Forum 2011.

Michael Chui, Markus Löffler, and Roger Roberts (2010), The Internet of Things.

McKinsey Quarterly 2010, Number 2.

Mark Blackburn (2010), Unused Servers Survey Results Analysis. Rev 2010-0, The

Green Grid.

Mark Aggar (June 2011), The IT Energy Efficiency Imperative.

www.microsoft.com/environment : Microsoft Corporation.

Jacques Bughin, Michael Chui, and James Manyika (August 2010), Clouds, Big Data, and

Smart Assets: Ten Tech-enabled Business Trends to Watch. McKinsey Quarterly.

Jonathan G. Koomey, Ph.D. (August 1, 2011), Growth in Data Center Electricity Use

2005 to 2010. www.analyticspress.com/datacenters.html : Analytics Press.

http://www.microsoft.com/environment

http://www.analyticspress.com/datacenters.html

Documents

Data Center Infrastructure Management (DCIM) of IT Systems