requestor name: information request round no: 1 to ...€¦ · 2.3.5 does the partnership (or faes)...

REQUESTOR NAME: BC Sustainable Energy Association and Sierra Club of B.C. INFORMATION REQUEST ROUND NO: 1 TO: FortisBC Alternative Energy Services (FAES) DATE: November 13, 2012 PROJECT NO: N/A APPLICATION NAME: Certificate of Public Convenience and Necessity Application for Telus Garden Thermal Energy System (TGTES) 1.0 Topic: Longevity of Data Centre Waste Heat supply

Reference: Exhibit B-1, CPCN Application

“The opportunity to make use of energy that would otherwise be wasted is attractive and has been shown to be superior to other heat source alternatives. Further, although all parties anticipate that the data centre will operate and produce waste heat for many years; prudent system design has included contingency planning in the event that use of other heat sources becomes necessary to continue to meet the needs of the Development.” [p.12, underline added]

1.1 Please provide evidence supporting the assertion that the Telus Data Centre will operate and produce waste heat (at the present location) for many years.

1.2 In the quotation above, FAES states: “prudent system design has included contingency planning in the event that use of other heat sources becomes necessary to continue to meet the needs of the Development.” [underline added] Please describe these contingency plans. Are the contingency plans limited to obtaining steam from Central Heat Distribution Ltd.?

2.0 Topic: Contract terms re Data Centre Waste Heat supply Reference: Exhibit B-1, CPCN Application

2.1 What commitment does FAES, or the Partnership, have from Telus that the Data Centre Waste Heat will be available and provided to the TGTES over the lifetime of the project?

2.2 What contracts or agreements set out the terms and conditions governing the supply of Data Centre Waste Heat to the Partnership? Please specify the parties to these contracts or agreements.

2.3 Please describe all the material terms and conditions governing the supply of Data Centre Waste Heat to the Partnership (or to FAES).

2.3.1 Please confirm that the price of the Data Centre Waste Heat is zero (“free” – p.24).

2.3.2 Is the (zero) price of the Data Centre Waste Heat guaranteed for the lifetime of the TGTES? For some other period of time? Is Telus (or its successors) precluded from reopening the price of the Data Centre Waste Heat at some time in the future?

C2-2

markhuds

FAES Telus Gardens TES CPCN

BCSEA-SCBC IR1 November 13, 2012 FAES Telus Garden TES of 5

2.3.3 Are the Partnership’s (or FAES’s) contractual rights regarding the Data Centre Waste Heat protected in the event of a change of ownership of Telus?

2.3.4 What terms and conditions apply in the event that the Data Centre Waste Heat becomes permanently unavailable, for example due to the Telus Data Centre being moved to a different location?

2.3.5 Does the Partnership (or FAES) have a right, or obligation, to receive all of the Data Centre Waste Heat? Does Telus have a right to sell or otherwise transfer some of the Data Centre Waste Heat to a third party?

3.0 Topic: Longevity of Data Centre Waste Heat Reference: Exhibit B-1, CPCN Application, Appendix D, Cobalt Screening Study, 7.4 Option #4: Data Centre Waste Heat

“The Data Center rejects a base heat load of 1,400kW at a reasonably constant rate 24/7, year round.” [p.2 of 43]

3.1 Did the Cobalt study address the availability of Data Centre Waste Heat over the lifetime of the project?

3.1.1 If so, what were the results? What specific assumptions and/or sources of data were utilized in this estimate?

3.1.2 If not, why not?

3.2 Please provide a graph and table showing the base heat load in kW produced by the Telus Data Centre over the past, say, ten years, and forecasted for the lifetime of the project.

3.3 How will the amount of Data Centre Waste Heat in future years be affected by:

3.3.1 Telus Data Centre energy efficiency improvements due to changes in server technology or changes in energy management practices, and

3.3.2 increases or decreases in the number of servers and other heat-producing equipment within the Telus Data Centre?

4.0 Topic: Forecast of Data Centre Waste Heat Reference: “ECOS Final Report: Canadian market Analysis for Servers and Data Centres, Nov 5, 2012” (Ecos Report), Attachment BCSEA IR 4.0

4.1 Is FAES familiar with the Ecos Report? Does FAES agree that the Ecos Report provides useful information regarding energy conservation and efficiency opportunities for data centres in Canada?

4.2 Please discuss the extent to which the energy conservation and efficiency measures identified in the Ecos Report are applicable to the Telus Data Centre? Have these measures already been implemented in the TDC?


What plans does Telus have to implement energy efficiency and conservation measures at the TDC? Would implementing energy conservation and efficiency measures at the TDC reduce the Data Centre Waste Heat significantly?

4.3 Please discuss whether the use of Data Centre Waste Heat for the TGTES reduces the motivation to implement energy conservation and efficiency measures at the TDC.

“One fundamental area where energy can be saved in data centres is in server power supplies [SPS]… In actual operation, [SPSs] can waste 20 percent to 30 percent of the electricity that flows through them, turning it into heat during normal operation. … [SPSs] exist today that can achieve greater than 90 percent operational efficiency.”” [Ecos Report, p 26]

4.4 What percentage of the Telus Data Centre SPSs have been brought up to 2012 standards?

4.5 What impact on available energy (waste heat) will result if the percentage of SPSs at the TDC is substantially increased?

“Comatose servers are those that run applications no longer needed or run no applications at all, yet remain installed and operating continuously. …[S]uch servers account for up to 30 percent of total servers installed in some data centres.” [Ecos Report, p 26]

4.6 What percentage of servers, if any, are “comatose” at the TDC?

4.7 What impact on available energy will result if the percentage of comatose servers at the TDC is substantially reduced?

“According to a recent study… the average server is no more than 6 percent utilized… Maximizing the utilization of existing servers [through virtualization] therefore represents one of the most significant opportunities for energy savings….Based on these estimates, virtualization can save 57 percent of energy use.” [Ecos Report, pp. 26-27]

4.8 What percentage of servers have been virtualized at the TDC?

4.9 What impact on available energy will result if the percentage of servers at the TDC is substantially increased?

“A significant improvement in efficiency is also possible when replacing a linear [regulator] with a switcher [power regulator]. Most linear regulators have efficiencies in the 60 to 70 percent range. … Efficiency for switches… typically exceeds 95 percent and can result in significant power savings.” [Ecos Report, p 28]

4.10 Has power regulation been addressed at the TDC? What percentage of regulators have been updated to switchers?

4.11 What impact on available energy will result if the percentage of updated regulators at the TDC is substantially increased?


5.0 Topic: Data Centre Waste Heat efficiencies References: 42U, Hot Aisle Containment Cooling Solutions, http://www.42u.com/cooling/hot-aisle-containment.htm Fontecchio, M. (2009, January 21). Data Center Air Conditioning Fans Blow Savings Your Way. Search Data Center: http://searchdatacenter.techtarget.com/news/article/0,289142,sid80_gci1345584,00.html Miller, R. (2007, September 24). Data Center Cooling Set Points Debated. Data Center Knowledge: http://www.datacenterknowledge.com/archives/2007/09/24/data-center-cooling-set-points-debated

“Up to 37% of a data center's energy costs can be attributed to cooling infrastructure.” [Fontecchio]

“Data center managers can save 4 percent in energy costs for every degree of upward change in the set point….If you’re running at 68 °F [20°C], you’re running at the bottom level of most of those ranges. ... There’s no reason why you can’t move to 78°C [26°C].” Mark Monroe, Director of Sustainable Computing at Sun Microsystems (JAVA). [quoted in Miller]

5.1 At what temperature is the TDC running its cooling setpoints?

5.2 What impact on available energy will result if the TDC was to implement substantially higher cooling setpoints?

The norm for data centre heat shedding is to provide hot and cold isles where cold air is supplied in a “cold” isle and removed in another “hot” isle. However, without containment of the isles the tendency is for the hot and cold air to mix through short circuiting, resulting in an average cold room temperature. One increasingly popular concept is to physically isolate hot isles from cold isles in order to prevent mixing of hot air with the cold supply. The aim is the reduction of supplied cold air volume and removed hot air volume. [Source: 42U, Hot Aisle Containment Cooling Solutions]

5.3 To what extent have the TDC supply and return fan volumes been optimized with variable frequency drives and isle containment?

5.4 Has Telus considered this strategy?

5.5 If Telus implements this strategy, to what extent would the resulting cooling system energy savings impact the available energy supply to the TGTES?

6.0 Topic: TES Load forecast Reference: Exhibit B-1, CPCN Application, Table 3-2: Summary of TGES Load Requirements

6.1 Please confirm that Domestic Hot Water (DHW) load is expected to be zero for Retail customers of the TES.


6.2 If so, does that imply that the TES is designed not to provide DHW to Retail customers? Why is the system designed not to provide DHW to Retail? How will Retail DHW be provided?

7.0 Topic: Residential cooling load Reference: Exhibit B-1, CPCN Application, Table 3-2: Summary of TGES Load Requirements

7.1 Please confirm that the TGTES is designed to provide space cooling to residential customers.

7.2 Please confirm that the PCI Marine Gateway TES is designed not to provide space cooling to residential customers.

7.3 Please explain why the Telus Garden TES provides residential space cooling. Was consideration given to the energy conservation benefits of not providing residential space cooling?

7.4 Has the availability of free Data Centre Waste Heat influenced the building design toward less than optimal energy efficiency?

8.0 Topic: Cost of Data Centre Waste Heat Reference: Exhibit B-1, CPCN Application

8.1 Will Telus save money by providing Data Centre Waste Heat to the TGTES rather than disposing of the waste in some other way?

8.2 Has FAES considered whether there is a rationale for Telus to pay the Partnership for the service of receiving Data Centre Waste Heat? If so, what was the conclusion? If not, why not?

TM

Portland, OR | San Francisco, CA | Seattle, WA | Durango, CO

Making a World of Difference

Final report presented to Natural Resources Canada

Canadian Market Analysis for Servers and Data Centres Submitted by: Catherine Mercier, Jonathan Hebert, Peter May-Ostendorp, Joel Watkins, and Mike Bailey Ecos 1199 Main Avenue Suite 242 Durango, CO 81301 970.259.6801

November 5, 2012

BILL

Text Box

Attachment BCSEA IR 4.0

Final Report | Canadian Market Analysis for Servers and Data Centres | November 5, 2012 i

Table of Contents

1. Executive Summary ................................................................................................................ 1 2. Introduction ............................................................................................................................. 2 3. Market Analysis ....................................................................................................................... 4

3.1. Literature Review .............................................................................................................. 4 Estimating Energy Used by Servers – World and U.S. ..................................................... 5 Definitions and Taxonomies ............................................................................................... 5

3.2. Characterizing the Canadian Server Market ................................................................ 10 Data and Methodology ....................................................................................................... 10 Calculating the Installed Base of Servers ........................................................................ 11 2. Canadian Server Market by CPU Type: x86 vs. Non-x86 Servers .............................. 13 3. Canadian Server Market by Form Factor ..................................................................... 14 4. Canadian Server Market by Application ....................................................................... 14

3.3. Annual Sales, Electricity Consumption and GHG Emissions .................................... 15 Annual Shipment Data by Unit .......................................................................................... 15 Annual Electricity Consumption Estimates ..................................................................... 18 Annual GHG Emissions ..................................................................................................... 21

3.4. Trends of Server Growth and Associated Electricity Use in Canada ........................ 22 3.5. Canadian Server Market Contact List ........................................................................... 26 3.6. Conclusion ...................................................................................................................... 26

Implications and Recommendations ................................................................................ 26 4. References ............................................................................................................................. 29 5. Appendices ............................................................................................................................ 32

Appendix 1: Glossary ............................................................................................................ 32 Appendix 2: Estimating Energy Use by Servers in Canada – Previous Works ............... 33 Appendix 3: IDC Canada Data Source ................................................................................. 36 Appendix 4: Workload Taxonomy (IDC Canada, 2008a) .................................................... 37 Appendix 5: CPU Type Taxonomy (IDC Canada, 2008a) .................................................... 38 Appendix 6: Calculation for % of Replacement and Average Age of Installed Base ..... 39

Final Report | Canadian Market Analysis for Servers and Data Centres | November 5, 2012 ii

Index of Figures

Figure 1: Scope of this Study .................................................................................................................... 2 Figure 2: Ecos estimate of Canadian Installed Base of Server by Server Class, 2004-2007 ............ 12 Figure 3: Ecos Estimate of Installed Base of Servers by Application in 2007 .................................... 15 Figure 4: Schematic of Server Energy Use Modeling Approach (Koomey, 2007a, U.S. EPA, 2007) 18 Figure 5: Ecos Estimate of Electricity Use by Canadian Servers by Server Class (MWh) ................ 20 Figure 6: Annual Electricity Consumption by Major Application/Workload ....................................... 21 Figure 7: Ecos Forecasted Installed Base of Servers by Server Class, 2008-2012 ........................... 23 Figure 8: Latitude for Energy Efficiency Improvements ....................................................................... 29

Index of Tables

Table 1: Data Centre Definitions ................................................................................................................ 6 Table 2: Server Categorization by Size ..................................................................................................... 8 Table 3: Ecos Estimate of Canadian Server Installed Base by Server Class, 2004-2007 .................. 12 Table 4: Ecos estimate of Installed Base of Servers in Canada by Server CPU Type ....................... 13 Table 5: Ecos Estimate of Canadian Shipment of Servers by Province ............................................. 16 Table 6: Ecos Estimate of Canadian Server Shipments by Application ............................................. 17 Table 7: Ecos estimate of Replacement vs. new sales ......................................................................... 17 Table 8: Ecos Estimate of Average Power and Energy Consumption by Server Class in Canada .. 18 Table 9: Ecos Estimate of Annual Electricity Used by Servers in MWh .............................................. 19 Table 10: GHG Emissions in Thousands of kg of CO2 eq. .................................................................... 22 Table 11: Ecos Forecasted Canadian Server Installed Base, by Server Class .................................. 22 Table 12: Ecos Estimated Canadian Server Installed Base and Growth Rates in 2007 and 2012 .... 23 Table 13: Forecasted Annual Electricity Used by Servers in MWh, 2007-2012 .................................. 25

Final Report | Canadian Market Analysis for Servers and Data Centres | November 5, 2012 1

1. Executive Summary

This report was commissioned by Natural Resources Canada (NRCan). The objective of this study is to assess the current trends in energy use and related operational costs for servers and data centres in the Canadian market, and to outline existing and emerging opportunities for improved energy efficiency. This information will enable the Office of Energy Efficiency (OEE) and NRCan to conduct economic, energy savings and environmental impact analysis and to aid the design of market transformation programs for servers in Canada. Ecos relied on IDC Canada data provided by NRCan to perform this analysis.

The following table is required by the statement of work, and contains the key statistics from the report.

Mandatory Analysis Information Comments

Energy source used Electricity

Sector Industrial

Annual shipments (units)

2002: 116 054 2003: 130 017 2004: 158 578 2005: 172 934 2006: 178 719 2007: 174 706

Source: IDC Canada, Quarterly Server Tracker ,2008a

Installed base (units)

2004: 567 839 2005: 628 600 2006: 693 523 2007: 745 409 2008: 777 415 2009: 793 425 2010: 803 848 2011: 818 395 2012: 839 296

Source: Ecos’ analysis

Forecast data (server shipments) (units)

2008: 178 389 2009: 183 259 2010: 187 902 2011: 191 581 2012: 198 483

Source: IDC Canada, Quarterly Server Tracker, Forecast View, 2008a

Average life expectancy (years)

x86 servers: 4.42 years Non-x86 servers: 4.75 years

Source: IDC Canada, Infrastructure Hardware Research, 2008b

Incremental annual energy savings (MWh) for volume servers

2009: 249 140 2010: 504 100 2011: 764 188 2012: 1 033 835

Estimated annual energy consumption of popular, low efficiency unit (volume server) 1 963 kWh per volume server

Estimated annual energy consumption of popular, high efficiency unit (volume server)

1 463 kWh per volume server


2. Introduction

The amount of electricity used by data centres in Canada has risen significantly in recent years. This increase in data centre energy use is part of a world-wide trend; other studies around the world now show data centres as one of the fastest growing electrical end uses. Natural Resources Canada (NRCan) has estimated that energy use by servers and data centres has doubled in the past five years and is expected to double again to close to 10 billion kWh by 2011.1 A number of large data centre operators are evaluating Canada as a possible location for major projects, ensuring sector growth for years to come. Many factors, including the low electricity costs and the cool temperatures in Canada, could explain their interest.2

Figure 1 depicts the main power-consuming components of a data centre. The largest single power consuming component is the server. Therefore, the scope of this report is limited to the server, omitting the associated cooling and auxiliary equipment, data storage, and network equipment. In order to quantify the entire data centre power consumption, similar research on the cooling and auxiliary equipment, data storage and network equipment should be conducted.

Figure 1: Scope of this Study

Source: Modified from Koomey, 2007a

Ecos conducted a preliminary analysis of the Canadian server market on behalf of NRCan in January 2008 (see Appendix 2). The first analysis, or Phase I, included rough estimates of the Canadian server installed base and energy use based on a top-down approach, scaling U.S. data to the Canadian market. Phase I took the ratio of the Canadian and U.S. economies in terms of gross domestic products, which is eight percent, and estimated that the Canadian server market is therefore eight percent of the U.S. server 1 NRCan, 2008 (internal estimates) 2 http://www.datacenterknowledge.com/archives/2008/Mar/26/manitoba_new_frontier_for_huge_data_centers.html

C&A for networking

C&A for data

storage

Cooling and auxiliaries (C&A) associated with

server power

Network equipment power

Data

storage power

Server power

Focus of this study

Total data center power


market in terms of shipments. Ecos concluded Phase I by recommending that NRCan purchase Canadian server market research data from IDC Canada to support this market analysis.

This report constitutes Phase II, which improves on Ecos’ previous analysis by estimating Canadian server installed base and energy use using Canadian historical and forecasted market data from IDC Canada.

The resulting Phase II Market Analysis is divided into six main sections:

Literature review

Characterizing the Canadian server market

Annual sales of servers, electricity consumed annually and annual GHG emissions

Trends of server growth and associated electricity use in Canada

List of contact information for Canadian server market information sources

Conclusion - Implications and Recommendations


3. Market Analysis

3.1. Literature Review

The literature review uncovered no reports that focus on Canadian server or data centre usage. However, the following reports have been completed for the United States and the global market. These reports are extremely relevant in assessing the validity of energy use models for the Canadian server and data centre market:

EPA Report to Congress and Data Center Energy Efficiency (U.S. EPA, 2007): The U.S. Environmental Protection Agency (EPA) developed this report in response to the request from Congress stated in Public Law 109-431. This report assesses opportunities for energy efficiency improvements for government and commercial computer servers and data centres in the U.S.

Estimating Total Power Consumption by Servers in the U.S. and in the World (Koomey, 2007a): This study estimates total electricity used by servers in the U.S. and the world by combining detailed data from IDC on the server installed base and shipments with measured data and estimates of the power used per unit for the most common server models in each server class in the U.S. and the world. Koomey shows that total world electricity used by servers and the associated cooling and infrastructure equipment doubled from 2000 to 2005, to approximately 123 billion kWh. The U.S. accounted for about 40 percent of that total.

Estimating Regional Power Consumption by Servers: A Technical Note (Koomey, 2007b): This technical note builds on previous analysis of total electricity used by servers in the U.S. and the world (Koomey 2007a) to estimate the regional distribution of electricity used by servers. The results reveal the predominance of the U.S. and Europe in total server electricity use as well as much greater than average annual growth rates in the Asia Pacific region (excluding Japan) over the 2000 to 2005 period. If current trends continue, the U.S. share of total world server electricity use will likely decline from 40 percent in 2000 to about one-third by 2010, while the Asia Pacific region (excluding Japan) will increase from 10 percent to about 16 percent. Other regions will maintain roughly constant shares of the world total.

Energy Consumption by Office and Telecommunications Equipment in Commercial Buildings-Volume I: Energy Consumption Baseline (Roth, et al. 2002): This study was prepared by Arthur D. Little for the U.S. Department of Energy in 2002. Arthur D. Little carried out a “bottom-up” study to quantify the annual electricity consumption of more than 30 types of non-residential office and telecommunications equipment, including computer servers.

Server Power Supplies (Ton and Fortenbery, 2005): Ton and Fortenbery gathered market data on servers to determine which types of configurations and manufacturers had the largest market penetration. Starting from the basic methodology established in the Roth et al. (2002) analysis, they constructed a revised estimate of the annual energy consumption of servers in the U.S.

Report on Energy Efficient Servers in Europe: Part 1, Energy consumption, saving potentials, market barriers and measures (Schäppi et al., 2007a) – This report presents the interim results of the international project Efficient Servers, which is conducted within the EU programme Intelligent Energy Europe. The project aims at demonstrating the high potential for energy savings and cost reductions for servers in practice and at supporting the market development for energy-efficient servers. In the first project phase, the European market for servers, the server


energy consumption and energy saving potentials were analysed. Furthermore the total electric power consumption and the saving potentials in data centres were estimated.

Report on Energy Efficient Servers in Europe: Part 2, Energy consumption, saving potentials, market barriers and measures (Schäppi et al., 2007b) – The report covers the project results on market barriers and on technical measures and options to support energy efficient solutions in practice.

Report on Energy Efficient Servers in Europe: Part 3, Energy efficiency criteria and benchmarks (Schäppi et al., 2007c) – This report classifies benchmark environments to assess the energy efficiency, and describe a measurement protocol to assess the energy efficiency in general.

Energy Efficiency in Data Centers: A New Policy Frontier (Loper and Parr, 2007) – This report discusses various energy saving opportunities and recommends a range of government policies and programs to encourage improvements to the energy efficiency of technologies and practices in data centres.

Estimating Energy Used by Servers – World and U.S.

There are several estimates of the amount of electricity used by servers and data centres (Kawamoto et al., 2001, Mitchell-Jackson et al., 2001, Roth et al., 2002, Ton and Fortenbery, 2005, Koomey, 2007a).

In 2002, Roth et al. used aggregate data from IDC by server class and measured power data from a representative server to estimate electricity used by each server class. This study also assessed the electricity used by data storage systems and network equipment.

Ton and Fortenbery (2005) used a similar approach with updated data to estimate electricity used by server class.

In a recent study, Koomey (2007a) estimated total electricity used by servers in the U.S. and the world. This study focused on the server loads and the infrastructure energy use associated with those servers. This analysis relied on detailed data from IDC installed base and shipments of servers in combination with measured data and estimates of the power used per unit of the most common server models in each server class in the U.S. and the world. Koomey reported a U.S. server installed base of more than 10.3 million, almost twice the 2000 installed base of 5.6 million. Total electricity used by servers in the U.S. in 2005 is estimated to be about 23 billion kWh. If electricity used by cooling and auxiliary equipment is included, that total rises to 45 billion kWh.

Definitions and Taxonomies

Data Centre Definitions

There are no government or private sector umbrella organization covering all data centres, however, definitions are available from a number of sources: ACEEE and CECS 2001; Aebischer et al. 2002; Brown et al. 2001; Gruener 2000; Intel 2002; Mitchell-Jackson 2001. Table 1 summarizes some of the relevant definitions of data centres.


Table 1: Data Centre Definitions

Organization Definition Source

U.S. Environmental Protection Agency

“A data center contains primarily electronic equipment used for data processing (servers), data storage (storage equipment), and communications (network equipment)*. Collectively, this equipment processes, stores, and transmits digital information and is known as ’information technology’ (IT) equipment. Data centers also usually contain specialized power conversion and backup equipment to maintain reliable, high-quality power, as well as environmental control equipment to maintain the proper temperature and humidity for the IT equipment.”

“This study excludes from the definition of “data center” any facilities that are primarily devoted to communications (e.g., telephone exchanges), including network equipment located in telecom data centers. The definition does, however, follow market research firm IDC’s convention of including in the definition any room that is devoted to data processing servers, i.e., server closets and rooms (Bailey et al. 2006).”

U.S. EPA 2007. Report to Congress on Server and Data Center Energy Efficiency Public Law 109-431.

Telecommunications Industry Association

“Data center: a building or portion of a building whose primary function is to house a computer room and its support areas.”

Telecommunications Infrastructure Standard for Data Centers. April 12, 2005.1

Lawrence Berkeley National Laboratory

In “High-performance data centers- A research roadmap,” the Lawrence Berkeley National Laboratory adopts a broad definition of the term data centre. As mentioned in the report, “we generally use the term data centre to be a facility that contains concentrated equipment to perform one or more of the following functions: store, manage, process and exchange digital data and information. We do not consider spaces that primarily house office computers, including individual workstations, servers associated with workstations, or small server rooms, to be data centers.”

Tschudi, Xu, Sartor and Stein, 2003

Code of Conduct on Data Centres Version 0.7

The term “data centres” includes all buildings, facilities, offices and rooms which contain enterprise servers, server communication equipment, cooling equipment and power equipment, and provide some form of data service (e.g., large-scale mission-critical facilities all the way down to small server rooms located in office buildings).

European Commission, 2007

1 This Standard specifies the minimum requirements for telecommunications infrastructure of data centres and computer rooms including single-tenant enterprise data centres and multitenant Internet hosting data centres. The topology proposed in this document is intended to be applicable to any size data centre. For further information see: http://ftp.tiaonline.org/TR-42/Tr425/Public/TR425-05-10-010a_working_dictionary.pdf


Data Centre Categories/Taxonomy

There are four common ways to categorize data centres.

1. Categorization by End-Application (Koomey et al., 2005) • Multi-tenant hosting facilities, which include data centres owned by third parties that house

servers owned by companies (collocation), as well as data centres that sell server services to companies that do not want to manage their own servers (managed hosting).

• Corporate or enterprise data centres, owned by corporations and managed in-house. These data centres are often housed within existing facilities and may make up only a small portion of the total floor area associated with those facilities.

• Institutional and government data centres, owned and operated by federal, state and local government or by non-profit institutions.

• Educational data centres, serving students and faculty in post-secondary institutions.

2. Categorization by Size or Square Footage Data centres range in size from small rooms (server closets of less than 20 square meters) within a conventional building to large buildings (enterprise class data centres in the hundreds of square meters) dedicated to housing servers, storage devices and network equipment, with the largest being called enterprise-class. Large data centres are becoming increasingly common as smaller data centres consolidate (Carr, 2005). As summarized in Table 2, server closets and server rooms can have significantly different IT equipment and infrastructure characteristics than larger data centres.


Table 2: Server Categorization by Size

Space type Typical size

Typical IT equipment characteristics

Typical site infrastructure system characteristics

Server closet <200 ft2 1-2 servers No external storage

Typically conditioned through an office HVAC system. To support VoIP and wireless applications, UPS and DC power systems are sometimes included in servers maintained as for other data centre types. HVAC energy efficiency associated with server closet is probably similar to the efficiency of office HVAC systems.

Server room <500 ft2 A few to dozens of servers No external storage

Typically conditioned through an office HVAC system, with additional cooling capacity, probably in the form of a split system specifically designed to condition the room. The cooling system and UPS equipment are typically of average or low efficiency because there is insignificant economy of scale to make efficient systems more first-cost competitive.

Localized data centre <1 000 ft2

Dozens to hundreds of servers Moderate external storage

Typically used for underfloor or overhead air distribution systems and a few in-room CRAC units. CRAC units in localized data centres are more likely to be air-cooled with constant-speed fans, and are thus relatively low efficiency. Operational staff is likely to be minimal, which makes it likely that equipment orientation and airflow management are not optimized. Air temperature and humidity are tightly monitored. However, power and cooling redundancy reduce overall system efficiency.

Mid-tier data centre <5 000 ft2

Hundreds of servers Extensive external storage

Typically use under-floor air distribution and in-room CRAC unit. The larger size of the centre relative to those listed above increases the probability that efficient cooling (e.g., a central chilled water plant and central air handling units with variable speed fans) is used. Staff at this size data centre may be aware of equipment orientation and airflow management best practices. However, power and cooling redundancy may reduce overall system efficiency.

Enterprise-class data centre

5 000 + ft2 Hundreds to thousands of servers Extensive external storage

The most efficient equipment is expected to be found in these large data centres. Along with efficient cooling, these data centres may have energy management systems. Equipment orientation and airflow management best practices are most likely implemented. However, enterprise-class data centres are designed with maximum redundancy, which can reduce the benefits gained from the operational and technological efficiency measures.

Source: U.S. EPA, 2007


3. Categorization per Uptime Institute One of the main obstacles to the adoption of energy efficiency solutions in data centres has been a perception that some energy-efficiency strategies may have a negative effect on data centre performance, including reliability, which is a key attribute of high-quality data centre equipment. Manufacturers will not compromise performance or reliability to achieve energy-efficiency improvements (U.S. EPA, 2007). Further research is therefore needed to identify areas of uncertainty about performance and reliability and provide information to manufacturers, purchasers, and operators.

The Uptime Institute Tier Classification system is based on performance standards and site infrastructure. Uptime’s classification has been in use since 1995 and has been updated in 2008.

Tier 1: composed of a single path for power and cooling distribution, without redundant components, providing 99.671 percent availability.

Tier II: composed of a single path for power and cooling distribution, with redundant components, providing 99.741 percent availability

Tier III: composed of multiple active power and cooling distribution paths, but only one path active, has redundant components, and is concurrently maintainable, providing 99.982 percent availability

Tier IV: composed of multiple active power and cooling distribution paths, has redundant components, and is fault-tolerant, providing 99.995 percent availability.

For more details of the Uptime Classification System classification, see Turner IV and al., 2008.

Server Definitions

As mentioned in the introduction, servers are the largest energy-using components of data centres. A server generally provides common functions to a group of users or performs back-end processing invoked on a scheduled basis or by other computers (Roth et al., 2002).

ENERGY STAR ® is currently developing a new product specification for enterprise servers. Since releasing its Draft 1 server specification in February 2008, the U.S. EPA has engaged in several industry discussions including a stakeholder online meeting on April 1, 2008. During the online meeting, the U.S. EPA discussed several key stakeholder comments and next steps toward releasing a subsequent draft. One of the next steps included revising and releasing a draft definition and scope document, which was distributed to stakeholders for review and comment on April 25. The goal in distributing a revised definitions document prior to Draft 2 document was to develop a consensus regarding the types of products to be covered by the specification. On July 9, 2008, in Redmond WA, the U.S. EPA held a stakeholder meeting to discuss with manufacturers and other interested stakeholders the latest developments for the ENERGY STAR ® server specification under development. Following discussions held during the Redmond meeting and comments and data received over the last few weeks, the U.S. EPA released its Draft 2 server specification on August 15, 2008. The computer server definition has been revised to address the full range of servers in the marketplace that are used in data centres and business environments. Below is the latest computer server definition, as proposed in the ENERGY STAR ® Draft 2 server specification:3

Computer Server: A computer that provides services and manages networked resources for client devices, e.g., desktop computers, notebook computers, thin clients, wireless devices, PDAs, IP telephones, other computer servers and other networked devices. Computer servers are sold through enterprise channels for use in data centers and office/corporate environments.

3 Further information can be found at http://www.energystar.gov/index.cfm?c=new_specs.enterprise_servers.


Computer servers are designed to respond to requests and are primarily accessed via network connections rather than through direct user input devices such as a keyboard, mouse, etc. In addition, computer servers must include all of the following characteristics:

Marketed and sold as a server;

Designed for and listed as supporting Server Operating Systems and/or Hypervisors, and targeted to run user-installed enterprise applications;

Designed and capable of supporting one or more processor sockets and/or one or more processor boards in the device; and

Support for error-correcting code (ECC) and/or buffered memory (including both buffered DIMMs and buffered on board (BOB) configurations).

Server Subcategories/Taxonomy

Servers themselves can be categorized in many ways (e.g., by shipments, revenue, geographic region, manufacturer, price range, operating system, processor, U-rating,4 architecture, etc.). IDC’s Worldwide and Regional Quarterly Server Tracker and Forecast research programs are probably the most comprehensive data source for statistics on server shipments. IDC’s server tracker research programs provide actual quarterly data for more than 15 data categories (including those mentioned above).

In his recent work, Jonathan Koomey (2007a, 2007b) used IDC’s data on aggregate installed base split into three server classes (volume, mid-range and high-end). IDC’s server class taxonomy segments the server market into three server classes (based on the cost of the system): volume server market with an average selling value (ASV) of below $25 000 U.S. per unit, mid-range enterprise server market with an ASV from $25 000 to $500 000 U.S. per unit and high-end enterprise server market with an ASV of $500 000 U.S. and above per unit.

3.2. Characterizing the Canadian Server Market

One of the main tasks of this market analysis was to present the most common types, sizes and applications of servers in Canada, which required information about the installed base of servers. The Canadian server market can be characterized by cost, CPU type, form factor or application.

Data and Methodology

We estimated the installed base of servers in Canada using data on shipments provided by IDC Canada (2008a). Established in 1984, IDC Canada has more than 30 analysts exclusively focused on the Canadian market and is the leading provider of research, consulting and marketing services to the Canadian information and communication technologies industry.

IDC Canada’s data included historical shipments of servers segmented by server class, vendor, and architecture into the Canadian market over the period of 2002 to 2007, as well as forecasts for server shipments through 2012. For more information on data provided by IDC Canada, see Appendix 3.

Ecos estimated the installed base of servers by adding the IDC Canada provided shipment data over the assumed useful life of servers. Guidance on the average life expectancy of shipped product was provided by IDC Canada through its Canadian Infrastructure Hardware research program (2008b).

4 The “U” rating is based on the height of the server: A 1-U server is 1.75 in. tall.


Server shipment data was not available for 2000 and 2001. To calculate the Canadian installed base of servers in 2005 and 2004, we used worldwide shipment data from J. Koomey’s research (2007a), and assumed that Canada represents about the same as the ratio of the 2002 Canadian server market and the worldwide server market in terms of shipments (see Appendix 7).

Calculating the Installed Base of Servers

1. Canadian Server Market by Server Class

IDC’s server class taxonomy segments the server market into three server classes (based on the cost of the system), where volume class servers have an average selling value (ASV) below $25 000 U.S., mid-range servers have an ASV between $25 000 and $500 000 U.S., and high-end servers have an ASV above $500 000 U.S. Table 3 shows Ecos’ estimates of the Canadian installed base of servers broken out by server class from 2004 to 2007.

As shown in Figure 2, Ecos estimates that volume servers represent 96 percent of the current total installed base of servers in Canada. According to Koomey (2007a), volume servers represented 96 percent of the U.S. installed servers on a unit basis in 2005. In the world, volume servers represented 95 percent of the installed servers on a unit basis in 2005 (Koomey, 2007a).


Table 3: Ecos Estimates of Canadian Server Installed Base by Server Class, 2004-2007

2000 2001 2002 2003 2004 2005 2006 2007 CAGR Shipments

Volume 101 960 103 388 108 660 122 722 152 443 166 766 173 690 170 361 9%

Mid-range 9 725 7 079 7 010 7 022 5 870 5 938 4 816 4 139 -10%

High-end 531 425 384 273 265 230 213 206 -12%

Total 112 215 110 892 116 054 130 017 158 578 172 934 178 719 174 706 9%

Installed base

Volume 532 374 596 385 663 750 718 692 11%

Mid-range 33 724 30 748 28 507 25 963 -8%

High-end 1 740 1 468 1 266 1 115 -13%

Total 567 839 628 600 693 523 745 409 10% To calculate server installed base for 2004 and 2005, we used worldwide shipment data from Koomey (2007a), and assumed that Canada represents about the same as the ratio of the 2002 Canadian server market and the worldwide server market in terms of shipments.

Figure 2: Ecos Estimates of Canadian Installed Base of Server by Server Class, 2004-2007

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Sources: To calculate server installed base for 2004 and 2005, we used worldwide shipment data from Koomey (2007a), and assumed that Canada represents about the same as the ratio of the 2002 Canadian server market and the worldwide server market in terms of shipments.


2. Canadian Server Market by CPU Type: x86 vs. Non-x86 Servers

Server shipment data provided by IDC Canada included information on the architecture of the servers being shipped into Canada. Understanding the architecture of the servers being shipped provides a useful insight into the types of applications for which the servers are being used and customer buying habits. Server architecture can be split into two simple groups: x86 servers – whose processors operate using instruction sets widely compatible with most Windows-compatible personal computers – and non-x86 servers – whose processors operate using other types of instruction sets, such as CISC, RISC, and EPIC and are used for more proprietary and task-specific functions (see Appendix 5 for more information on IDC’s CPU type taxonomy). The generic x86 server architecture is similar to typical PC architecture and is usually compatible with PC software. Non-x86 servers include a wide range of server architectures and software platforms designed to perform a particular task-specific function, usually handling many instructions at the same time (e.g., database servers, IT infrastructure servers, network routers). The instruction sets used by non-x86 servers have specialized computer instruction sets that drive different operating systems usually aimed at optimizing to process large amounts of data.

Servers were traditionally designed around the non-x86 architecture but have been steadily shifting toward x86, utilizing typical processor instruction sets common to Intel and AMD processors. As shown in Table 4, the x86 servers represent 90 percent of the installed base of servers in Canada today. Forecasts show that this trend is likely to continue. The x86 servers are expected to account for over 94 percent of the Canadian installed base of servers by 2012.

Table 4: Ecos Estimates of Installed Base of Servers in Canada by Server CPU Type

Server Type 2007 2008 2009 2010 2011 2012 CAGR Non-x86 Server 74 550 66 165 57 753 51 081 47 692 46 428 -9%

x86 670 859 711 250 735 672 752 766 770 703 792 868 3% Total 745 409 777 415 793 425 803 848 818 395 839 296 2%

Dell’s chief technology officer (CTO), Kevin Kettler, has said that x86 server technologies will dominate the market in the years to come. He believes that x86 type scale-out server system design– incrementally building server system capacity over time – is preferable to overbuilding server systems, as is common, based on the projected system’s needs years out. Incrementally building a system reduces the amount of sunk cost needed for a system upgrade and can also save on operational expenses through energy savings. Dell’s CTO also predicts that rapidly improving processor technology and virtualization will contribute to the dominance of x86 server technologies.5

There are some dissenting opinions concerning the prevalence of x86 type scale-out system design architecture in the future of the data centre market.6 The argument is based on two premises: First, that enterprise type scale-up system designs are better for high input/output rates and large workloads; and second, that scale-out system designs are often obsolete and no longer fulfil the system’s needs by the time the infrastructure is updated.

5 http://searchservervirtualization.techtarget.com/news/article/0,289142,sid94_gci1226067,00.html# 6 Kevin Kettler advocates Dell’s focus on “scale-out” architecture, contrasting with the other three major vendors – IBM, Hewlett-Packard and Sun Microsystems – which focus on scale-out and scale up architectures. http://searchservervirtualization.techtarget.com/news/article/0,289142,sid94_gci1226067,00.html#


3. Canadian Server Market by Form Factor

Servers come in a number of physical form factors, including rack-optimized and blade servers. A rack-optimized server is a server designed to be installed in a framework called a rack. A blade server is a server chassis housing multiple, thin, modular electronic circuit boards, known as server blades. Blade servers integrate servers, storage, networking and applications into one system. In 2007, rack-optimized servers accounted for 62 percent of the Canadian server market in terms of shipments. In the future, the number of rack-optimized servers is expected to remain stable, while the number of blade servers grows. IDC Canada predicts that blade servers will account for 22.4 percent of the market in 2012 (2008b) (for more information on the Canadian server market trend, see section 3.4). The move towards more blade servers will tend to increase power density in data centers. With multiple, highly compact blade servers, a single blade server chassis can deliver more processing capacity than rack-optimized servers. This increased density can translate into higher power drawn per square foot. In general, for the same processing capacity, blade servers tend to be more energy efficient than individually rack mounted servers due to better utilization of shard components (blades tend to have shared power supplies, data storage, and I/O support). The associated total energy consumption will depend on how the overall processing capacity changes. On one hand, if the overall processing capacity stays the same, we can expect the total energy consumption to decrease as more blade servers are used. One the other hand, if the overall processing capacity increases, the total energy consumption could stay the same or even increase if the processing capacity increase is greater than the efficiency gains.

4. Canadian Server Market by Application

Servers are extremely versatile and can be used for applications ranging from the processing of financial transactions to handling digitized phone calls. IDC Canada provided historical annual shipment data split by the following applications for 2004 (see Appendix 4 for IDC’s Workload Taxonomy):

Business processing: Enterprise resource planning (ERP) (including enterprise wide line-of-business applications, other business commerce applications that facilitate business transactions or other task automation over networks, departmental transactional applications that run on servers but don't tie directly to other applications), customer relationship management (CRM), online transaction processing (OLTP), traditional legacy mainframe-type processes that execute business process transitions in a batch process

Decision support: data warehousing/data mart, data analysis/data mining Collaborative: email and applications that let users collaborate and share information Application/software: traditional application development work IT infrastructure: file/print sharing, networking, proxy catching, security, system management Web infrastructure: Web serving and streaming media Technical: scientific/engineering workloads Other: all other applications not covered by the above definitions

Assuming that the market share of each application remained constant over the period of 2002-2007, we calculated the Canadian estimated installed base of servers split by application; see Figure 3. Over 40 percent of servers in Canada are being used for IT infrastructure. The other categories pertinent to this research include business processing, decision support and Web infrastructure. The combined market share of these server categories makes up about 30 percent the Canadian server market; these categories are likely to consist of the data centre type IT systems infrastructure covered by this report.


Figure 3: Ecos Estimates of Installed Base of Servers by Application in 2007

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3.3. Annual Sales, Electricity Consumption and GHG Emissions

Ecos has outlined the quantitative effects of the Canadian server market by annual electricity usage and the respective GHG emissions by province. It is important to remember that the scope of this analysis only examines energy use as a function of electricity used by servers, excluding the associated cooling and auxiliary equipment needed to support IT equipment. The inclusion of infrastructure equipment would roughly double the overall energy use figures. Further energy analysis of data centres could include IT infrastructure energy usage. Canada’s unique northern climate might, for example, result in lower infrastructure-related energy use for cooling, causing servers to dominate the energy use equation.

Annual Shipment Data by Unit

Ecos used information on population size and distribution by province to split Canadian shipments by province/region. We then validated this assumption by comparing the results to a similar spread using gross domestic product by province, active population by province, computer system design and related services by province, as well as employment by major industry groups by province. Ecos determined that population density scaling provides an adequate view of server installed base across region. Our team also researched a number of other scaling methodologies based on installed footage of raised floor. We determined that the lack of information available for these alternate scaling methodologies outweighed any additional accuracy that those methods might provide. Table 5 summarizes annual sales by province and by the market as a whole.


Table 5: Ecos Estimates of Canadian Shipment of Servers by Province

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Newfoundland and Labrador 1 843 2 065 2 519 2 747 2 839 2 775 2 833 2 911 2 984 3 043 3 153

Prince Edward Island 496 555 677 738 763 746 762 783 802 818 848

Nova Scotia 3 361 3 766 4 593 5 009 5 176 5 060 5 167 5 308 5 442 5 549 5 749

New Brunswick 2 696 3 020 3 684 4 017 4 152 4 059 4 144 4 257 4 365 4 451 4 611 Quebec 27 286 30 568 37 283 40 659 42 019 41 075 41 941 43 086 44 178 45 043 46 666 Ontario 45 066 50 489 61 579 67 154 69 401 67 842 69 272 71 164 72 966 74 395 77 076 Manitoba 4 217 4 725 5 762 6 284 6 494 6 348 6 482 6 659 6 828 6 962 7 212 Saskatchewan 3 565 3 994 4 872 5 313 5 491 5 367 5 481 5 630 5 773 5 886 6 098 Alberta 11 842 13 267 16 181 17 646 18 236 17 827 18 202 18 699 19 173 19 549 20 253 British Columbia 15 309 17 151 20 919 22 813 23 576 23 046 23 532 24 175 24 787 25 272 26 183 Yukon Territory 111 125 152 166 171 167 171 176 180 184 190 Northwest Territories 153 171 209 228 235 230 235 241 247 252 261 Nunavut 108 121 147 161 166 162 166 170 175 178 185 Canada 116 054 130 017 158 578 172 934 178 719 174 706 178 389 183 259 187 901 191 582 198 483

Sources: Ecos used information on population size and distribution by province to split Canadian shipments by province/region.


IDC Canada also provided shipment data by application/workload for the year 2004. Ecos assumed that the market share of each application remained constant over the period of 2002-2007. Table 6 tabulates Canadian shipments by application.

Table 6: Ecos Estimates of Canadian Server Shipments by Application

2002 2003 2004 2005 2006 2007 Application Development 8 056 9 025 11 008 12 005 12 406 12 128 Business Processing 12 660 14 183 17 299 18 865 19 496 19 058 Collaborative 20 286 22 727 27 719 30 229 31 240 30 539 Decision Support 10 650 11 931 14 552 15 869 16 400 16 032 IT Infrastructure 48 766 54 634 66 635 72 668 75 099 73 412 Other 292 327 399 435 450 439 Technical 4 458 4 994 6 091 6 642 6 864 6 710 Web Infrastructure 10 886 12 196 14 875 16 221 16 764 16 388 Total 116 054 130 017 158 578 172 934 178 719 174 706

Replacement vs. New Sales

No systematic data on server retirement and replacement vs. new sales was available for Canada at the time this report was created. However, using shipment data provided by IDC Canada (IDC, 2008a), Ecos estimated that replacements are about 70 percent of total server sales; see Table 7. For detail information on how we estimated the replacement vs. new sales see Appendix 6.

Table 7: Ecos Estimates of Replacement vs. new sales

Years Shipment Replacement/ retirement/

Installed bases

Share of current shipments that are new sales

2007 174 706 123 874 745 409 About 30% Source: 2007 shipment data comes from IDC Canada’s Quarterly Server Tracker research program(IDC, 2008a)

Penetration Rate by Efficiency

Manufacturers of servers, processors and other components have increased the energy efficiency of their product over the last few years. According to the U.S. EPA (2007), in the U.S., energy efficient servers was estimated to represent 5 percent of volume server shipments in 2007 and expected to represent 15 percent of shipments in 2011.

Estimated Age of Equipment Use

The average age of the server installed base in 2007 is estimated to be 2.13 years (see Appendix 6). To calculate the average age of the server installed we used the server lifetime estimates provided by IDC Canada (IDC, 2008b). The server lifetime estimates are based on research conducted as part of IDC Canada’s Infrastructure Hardware program. It is important to note that many servers are never removed even when they are no longer needed, yet they continue to use electricity. The average age of the server installed base may therefore be underestimated.


Annual Electricity Consumption Estimates

Data and Methodology

Ecos determined the annual electricity consumption of servers in Canada using the approach used by J. Koomey to estimate the total energy consumption for the U.S. server market (Koomey, 2007a). Figure 4 below show a schematic of Koomey’s approach.

Figure 4: Schematic of Server Energy Use Modeling Approach (Koomey, 2007a, U.S. EPA, 2007)

# of installed base volume

servers in Canada

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First, the Canadian installed base of servers is split by server class into provinces/regions by using information on Canadian population distribution and size.

Next, average energy use per server is calculated by taking the average power used per server for the rest of the world in 2005 directly from Koomey (2007b),7 and multiplying the power usage by the number of hours in a calendar year (assuming that servers operated 100 percent of the year); see Table 8.

Table 8: Ecos Estimates of Average Power and Energy Consumption by Server Class in Canada

Volume Mid-range High-end

Average power use (watts) 224 598 8 378

Annual energy consumption per server (kWh)

1 963 5 241 73 433

Sources: Average power used estimates by server class come from Koomey (2007b). Energy consumption per server was calculated assuming 8 765 hours/year and that servers operated 100 percent of the year

7 Publicly available Canadian data on power use per server in Canada was not available for this analysis. Koomey’s general approach to determine the average power used per server was to develop a representative sample of the servers across markets and then use a combination of the rated input power from spec sheets, typical operational power available from manufactures, and a set of power usage multipliers to determine power use per unit, for a given generation of server hardware. He developed a weighted average power use figure based on the market share of each server and characterized the average power consumption for each class of servers – volume, midrange and high-end – for the U.S. and the world markets. Finally, he used a standard duty cycle of 100 percent, assuming that servers operated all year, to calculate the energy use.


Finally, we multiplied the estimated average energy used per server by the installed base by server class and province/region for the period 2004 to 2007 to calculate total electricity use by server class and province/region; see Appendix 8.

Estimated Annual Electricity Used by Servers in Canada – Results

Table 9 shows the resulting electricity consumption by server class and province/region for the period 2004-2007. As shown Table 8, high-end and mid-range servers consume significantly more power than volume servers, but Table 9 and Figure 5 show that, given the share of volume servers that make up the Canadian market, their energy use ultimately dominates

Table 9: Ecos Estimates of Annual Electricity Used by Servers in MWh

2004 2005 2006 2007 CAGR (2004-2007) Volume – total 1 045 243 1 170 919 1 303 181 1 411 051 11% Newfoundland and Labrador 16 602 18 598 20 698 22 412

Prince Edward Island 4 463 5 000 5 565 6 026 Nova Scotia 30 274 33 914 37 744 40 869

New Brunswick 24 282 27 201 30 274 32 780 Quebec 245 748 275 296 306 392 331 754 Ontario 405 892 454 695 506 055 547 944

Manitoba 37 982 42 549 47 355 51 274 Saskatchewan 32 113 35 974 40 037 43 351

Alberta 106 655 119 479 132 975 143 981 British Columbia 137 883 154 462 171 909 186 139 Yukon Territory 1 001 1 122 1 248 1 352

Northwest Territories 1 376 1 541 1 715 1 857 Nunavut 972 1 089 1 212 1 312

Mid-range – total 176 763 161 167 149 418 134 195 -8% Newfoundland and Labrador 2 808 2 560 2 373 2 131

Prince Edward Island 755 688 638 573 Nova Scotia 5 120 4 668 4 328 3 887



Alberta 18 037 16 445 15 246 13 693 British Columbia 23 318 21 260 19 710 17 702 Yukon Territory 169 154 143 129

Northwest Territories 233 212 197 177 Nunavut 164 150 139 125

High-end – total 127 797 107 710 92 930 81 863 -13% Newfoundland and Labrador 2 030 1 711 1 476 1 300

Prince Edward Island 546 460 397 350 Nova Scotia 3 701 3 120 2 692 2 371



Alberta 13 040 10 991 9 482 8 353 British Columbia 16 858 14 208 12 259 10 799 Yukon Territory 122 103 89 78

Northwest Territories 168 142 122 108 Nunavut 119 100 86 76

Total 1 349 802 1 439 795 1 545 528 1 627 108 7% Sources: Koomey’s data on power use for individual servers (Koomey, 2007b). Electricity use is calculated by multiplying the installed base by the average power used per server. Direct electricity consumption assumes 8 765hours/ year and 100 percent load.


Figure 5: Ecos Estimates of Electricity Use by Canadian Servers by Server Class (MWh)

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Sources: Koomey’s data on power use for individual servers (Koomey, 2007b). Electricity use is calculated by multiplying the installed base by the average power used per server. Direct electricity consumption assumes 8 765 hours/year and 100 percent load.

As of 2007, the electricity use attributable to servers in Canada is estimated at about 1 627 108 MWh, or 0.3 percent of the total Canadian electricity consumption. These findings concur with our phase one analysis (see Appendix 2). In our phase one analysis, we estimated that total electricity used by servers in Canada was about 1 800 000 MWh in 2005.

Recall, this phase II study focused on the servers only, and the total electricity used with servers in data centres also includes the electricity used by cooling and auxiliary equipment. If we assume as Koomey did (2007a) that every kWh of electricity use for IT loads means another kWh of electricity use for infrastructure, including cooling and auxiliary equipment, that increases the total electricity consumption to about 3 254 216 MWh.8 Koomey (2007a) estimated that electricity use of U.S. servers in 2005 was roughly 22 500 000 MWh. When electricity use for cooling and auxiliary equipment is included, that total rises to 45 000 000 MWh. Total electricity consumption for the world as a whole is about 61 400 000 MWh, or 122 900 000 MWh when electricity use for cooling and auxiliary equipment is included (Koomey, 2007b). The Canadian share of the world server electricity use would therefore be about 2.6 percent.

In 2007, volume servers were responsible for the majority of the electricity used by Canadian servers. With a CAGR of 11 percent, volume servers also experienced the greatest growth in electricity use among all server classes.

The electricity use by Canadian servers was disaggregated into eight workloads (see Appendix 4 for a definition of each workload). This allowed a better characterization of energy use by servers. Figure 6 shows the estimates of energy use by major application/workload.

8 Further research is needed for cooling and auxiliary equipment, so that the total electricity use of data centres can be estimated.


Figure 6: Annual Electricity Consumption by Major Application/Workload

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Sources: Installed base of servers was calculated by Ecos. Ecos used Koomey’s data on power use for individual servers (Koomey, 2007b). Electricity use is calculated by multiplying the installed base by the average power used per server. Direct electricity consumption assumes 8 765 hours/year and 100 percent load.

Annual GHG Emissions

Canada's greenhouse gas emissions vary from region to region. This is because the distribution of natural resources and heavily fossil fuel dependent industry within the country. Alberta, with an energy generation system that is predominantly coal-based, has the highest GHG intensity in Canada. The Atlantic region, which relies on coal and nuclear, has a GHG intensity that is somewhat lower than that of Alberta, and Quebec, Manitoba and British Columbia, where energy generation is dominated by hydro, have the lowest GHG intensities. Ontario lies somewhere between the two extremes, with its mix of hydro, nuclear and fossil fuels, and is very close to the Canadian average. To calculate GHG emissions by servers in Canada, Ecos therefore used different GHG intensities; see Table 10. GHG emissions were calculated by multiplying each province’s GHG intensity by the estimated electricity used by the server installed base in the province.


Table 10: GHG Emissions in Thousands of kg of CO2 eq.

2004 2005 2006 2007 GHG intensities g CO2 eq per kWh

Canada 314 094 343 621 368 264 387 703 Newfoundland and Labrador 63 69 74 78 3

Prince Edward Island 1 419 1 552 1 663 1 751 252 Nova Scotia 29 436 32 203 34 513 36 334 771

New Brunswick 12 065 13 200 14 146 14 893 394 Quebec 2 820 3 085 3 307 3 481 9.1 Ontario 112 614 123 201 132 036 139 006 220

Manitoba 671 734 786 828 14 Saskatchewan 33 289 36 419 39 031 41 091 822

Alberta 118 634 129 787 139 094 146 436 882 British Columbia 2 956 3 234 3 466 3 649 17 Yukon Territory 38 41 44 47 30

Northwest Territories 52 57 61 64 30 Nunavut 37 40 43 45 30

Sources: The GHG intensities by province is from Environment Canada, National Inventory Report, 1990-2005: Greenhouse Gas Source and sinks in Canada, http://www.ec.gc.ca/pdb/ghg/inventory_report/2005_report/a9_eng.cfm#ta9_1, August 14 2008.

3.4. Trends of Server Growth and Associated Electricity Use in Canada

The total number of servers in Canada is expected to grow with a CAGR of 2.4 percent from 745 409 units in 2007 to around 839 296 units by 2012, which is almost 1.5 times the number of the installed base in 2004; see Table 11. Current trends toward server virtualization, which allows IT demand to be met through fewer physical servers, may explain why the growth of the installed base is predicted to be slower in the next five years, compared to previous years. Virtualization technology can help improve server utilization by allowing multiple, software-based “virtual machines” to run on a single server, allowing that server to run two to 20 times the number of applications. Consequently, the base of “virtual servers” in Canada will continue to grow rapidly through virtualization software packages; however, the growth in the number of physical servers will slow because each new physical server might house multiple virtual servers.

As shown in Table 11 and Figure 7, volume servers represent 96 percent of the current total installed base of servers in Canada. Forecasts show that this trend is likely to continue. In 2012, Ecos expects that the volume servers will represent 97.5 percent of the Canadian installed base of servers.

Table 11: Ecos Forecasted Canadian Server Installed Base, by Server Class

2007 2008 2009 2010 2011 2012 CAGR Volume Server 718 692 753 841 771 815 783 443 798 104 818 536 2.6%

Mid-range Server 25 602 22 541 20 640 19 440 19 329 19 816 -5.0% High-end Server 1 115 1 033 970 964 962 944 -3.4%

Total 745 409 777 415 793 425 803 848 818 395 839 296 2.4%

Sources: The installed base of servers was estimated by adding the shipments over the assumed useful life of servers. The server life expectancy estimates by server class come from Ecos’ analysis of IDC’s server life expectancy data by CPU type.


Figure 7: Ecos Forecasted Installed Base of Servers by Server Class, 2008-2012

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Table 12: Ecos Estimated Canadian Server Installed Base and Growth Rates in 2007 and 2012

Server Class

Installed base in 2007

2007 Installed base

Composition Installed

base in 2012 2012 Installed

base Composition

Growth 2007-2012

Volume 718 692 96.4% 818 536 97.5% 14% Mid-range 25 602 3.4% 19 816 2.4% -23% High-end 1 115 0.2% 944 0.1% -16% Total 745 409 100% 839 296 100% 13%


As discussed previously, the move towards more blade servers will tend to increase power density in data centers. According to IDC, blade server shipments are expected to increase at a CAGR of 16.4 percent (IDC Canada, 2008a).This is expected to result in higher power densities in many data centres.

If the per unit energy consumption for blade servers follows the same trends as the energy use of rack-optimized servers, electricity use by servers in Canada will increase by 10 percent from 2007 to 2012, largely due to the increased sales of blade servers.

The forecasted electricity used by servers from 2007 to 2012, and the percentage growth rates in server electricity use from 2007 to 2012 are presented in Table 13.


Table 13: Forecasted Annual Electricity Used by Servers in MWh, 2007-2012

2007 2008 2009 2010 2011 2012 CAGR Volume – total 1 411 051 1 480 061 1 515 351 1 538 181 1 566 965 1 607 081 2.6%

Newfoundland and Labrador 22 412 23 508 24 068 24 431 24 888 25 525

Prince Edward Island 6 026 6 320 6 471 6 568 6 691 6 863 Nova Scotia 40 869 42 867 43 890 44 551 45 384 46 546

New Brunswick 32 780 34 383 35 203 35 733 36 402 37 334 Quebec 331 754 347 979 356 276 361 643 368 411 377 842 Ontario 547 944 574 742 588 445 597 311 608 489 624 067

Manitoba 51 274 53 782 55 065 55 894 56 940 58 398 Saskatchewan 43 351 45 471 46 555 47 257 48 141 49 374

Alberta 143 981 151 023 154 624 156 954 159 891 163 984 British Columbia 186 139 195 242 199 897 202 909 206 706 211 998 Yukon Territory 1 352 1 418 1 452 1 474 1 501 1 540

Northwest Territories 1 857 1 948 1 995 2 025 2 063 2 116 Nunavut 1 312 1 376 1 409 1 430 1 457 1 494

Mid-range –total 134 195 118 150 108 184 101 895 101 314 103 865 -5.0% Newfoundland and

Labrador 2 131 1 877 1 718 1 618 1 609 1 650

Prince Edward Island 573 505 462 435 433 444 Nova Scotia 3 887 3 422 3 133 2 951 2 934 3 008



Alberta 13 693 12 056 11 039 10 397 10 338 10 598 British Columbia 17 702 15 586 14 271 13 442 13 365 13 701 Yukon Territory 129 113 104 98 97 100

Northwest Territories 177 156 142 134 133 137 Nunavut 125 110 101 95 94 97

High-end –total 81 863 75 847 71 255 70 813 70 611 69 328 -3.3% Newfoundland and

Labrador 1 300 1 205 1 132 1 125 1122 1 101

Prince Edward Island 350 324 304 302 302 296 Nova Scotia 2 371 2 197 2 064 2 051 2 045 2 008



Alberta 8 353 7 739 7 271 7 226 7 205 7 074 British Columbia 10 799 10 005 9 400 9 341 9 315 9 145 Yukon Territory 78 73 68 68 68 66

Northwest Territories 108 100 94 93 93 91 Nunavut 76 71 66 66 66 64

Total 1 627 108 1 674 058 1 694 790 1 710 889 1 738 891 1 780 275 1.8% Sources: Koomey’s data on power use for individual servers (Koomey, 2007). Electricity use is calculated by multiplying the installed base by the average power use per server.


3.5. Canadian Server Market Contact List

Contact information on Canadian server market information sources has been provided as an electronic Excel spreadsheet attached to the electronic version of this report.

3.6. Conclusion

Implications and Recommendations

The amount of electricity used by data centres in Canada has risen significantly in recent years. In this analysis, we relied on IDC shipment data and server life assumptions to assess current trends in energy use for servers and data centres in the Canadian market. As discussed previously, many servers are never removed even when they are no longer needed, and continue to use electricity longer than their useful life. Therefore, the installed base and the power impact of servers may be underestimated in this study.

Previous work indicates substantial latitude for improving the design and operation of servers in data centres.

Improve Efficiency of Power Supplies

One fundamental area where energy can be saved in data centres is in server power supplies. These devices convert alternating current (ac) power at the plug (often at “high line” voltages of 208 V to 230 V) into direct current (dc) power that server can use (often at 12 V or lower). In actual operation, server power supplies can waste 20 percent to 30 percent of the electricity that flows through them, turning it into heat during normal operation (Ecos and EPRI, 2008).

Server power supplies exist today that can achieve greater than 90 percent operational efficiency (Ecos and EPRI, 2008). According to Ecos and EPRI (2008), annual savings from 73 to 195 kWh/ yr could be achieved per volume server depending on the level of efficiency of the power supply.

The national electricity savings with 719 000 volume class servers in use is about 52-140 GWh.

Decommissioning Comatose Servers

Comatose servers are those that run applications no longer needed or run no applications at all, yet remain installed and operating continuously. According to a recent study jointly conducted by Uptime Institute and McKinsey (McKinsey and Company, 2008), such comatose servers account for up to 30 percent of total servers installed in some data centres. One way to improve data centre efficiency is simply to decommission comatose servers. Turning off comatose servers and removing them from use can save approximately 2 628 kWh per server removed.

The national electricity savings with 719 000 volume class servers in use would be about 567 GWh.

Virtualization of Existing Servers

The widespread underutilization of servers is one of the most often-cited reasons for suboptimal energy efficiency in data centres.

According to a recent study jointly conducted by the Uptime Institute and McKinsey (McKinsey and Company, 2008), the average server is no more than 6 percent utilized. The tendency to waste server


capacity by overbuilding systems and underutilizing them is widely spread in the general server market. Poor planning, overbuilding and lack of infrastructure management are the key contributors to energy waste in the data centre market. Facility utilization, meaning the cooling and floor layout and other infrastructure facilities, was found to be on average only 56 percent utilized. The report also concludes that with a growing need for data centres and IT infrastructure it will become an increasingly important issue for companies that seek to prevent IT costs from cutting into their profitability. Uptime expects that the use of virtualization technology will improve server utilization as well as reduce the number of physical servers needed (McKinsey and Company, 2008).

Maximizing the utilization of existing servers therefore represents one of the most significant opportunities for energy savings. Virtualization technology can help improve utilization by allowing multiple, software-based “virtual machines” to run on a single server, allowing that server to run two to 20 times the number of applications (depending on application usage and demand on server computational capacity).

The savings from virtualization come first from eliminating comatose servers and then from consolidating computation from servers that are underutilized. Based on these estimates, virtualization can save 57 percent of energy use.

The national electricity savings with 719 000 volume class servers in use is about 1 581 GWh.

Processor Efficiency Improvements

Three key trends in server microprocessor technology can significantly reduce server energy use in the near future (Loper and Parr, 2007; U.S. EPA, 2007): the shift to multiple core processors; the development of dynamic frequency and voltage scaling capabilities; and the development of virtualization capabilities.

Organizations like the Standard Performance Evaluation Corporation (SPEC) are working on methods to measure processor performance per watt as a metric for energy efficiency.9 This should help IT managers to consider power characteristics along with other selection criteria to increase the efficiency of data centres.

Memory Power Reductions

Recent advancements in memory technology may also help to improve the energy efficiency of future servers. Ecos and EPRI found that in desktop computer differences in memory efficiency can save up to 1-2 watts on the motherboard (Beck et al, 2008).10 Assuming that servers are operating 100 percent of the time, this would translate into 8 to 16 kWh of annual savings per server.

The national electricity savings with 719 000 volume class servers in use is about 6 to 12 GWh.

Improve Efficiency of Secondary Power Supplies, including Voltage Regulator Downs (VRDs), Point-of-Load Converters (POLs) and other Power Supplies Integrated into Motherboard

Secondary power supplies are devices that convert dc electricity from the primary power supply into even lower dc voltages. Electricity entering servers is converted from ac to low-voltage dc power in the server power supply unit. The low-voltage dc power is used by the server’s internal components of, such as the central processing unit (CPU), memory, disk drives, chipset, and fans. The dc voltage serving the CPU is adjusted by the server’s secondary power supply before reaching the CPU.

9 http://www.spec.org/power_ssj2008/ 10 http://efficientproducts.org/reports/computers/1337_EnergyEfficientComputerWhitePaper_FINAL_20Mar08.pdf


Although the server’s secondary power supply might only consume 50 to 80 watts of power itself, its power passes through a large number of other power conversion devices, including the primary power supply and the uninterruptible power supply, each with its own power conversion losses (EPRI and Ecos, 2008). Saving a watt at the secondary power supply saves an additional 0.3 to 0.5 watts in upstream devices because less power passes through them. Using best available measurement data and knowledge of secondary power supply design, EPRI and Ecos (2008) found that for a server that is effectively utilized 100 percent of the time, the annual energy savings opportunity for thicker copper traces is estimated at about 28 kWh per year.

A significant improvement in efficiency is also possible when replacing a linear with a switcher. Most of the linear regulators have efficiencies in the 60 to 70 percent range (EPRI and Ecos, 2008). Efficiency figures for switchers often quoted by manufacturers (e.g. National semiconductor, Maxim IC, or Linear Technologies) in their dataset typically exceeds 95 percent and can result in significant power savings (EPRI and Ecos, 2008).

Reduce Hard Drive Power Consumption

A number of new and emerging technologies promise to decrease hard drive power consumption. Osterberg predicts that the average power use per drive will decrease by approximately 7 percent from 2007 to 2010 as a result of these trends (U.S. EPA, 2007).

Technologies currently available can yield significant energy savings by allowing servers to “sleep” during times when data centre traffic is low (for example, in financial institutions where the servers only need to be on during active trading hours).

Ecos and EPRI carried out tests on desktop computers to evaluate the performance and energy consumption differences between efficient drives and a more conventional 7,200 rpm, 3.5-inch desktop hard drive, and found that some technologies used 5W to 6W less power than the stock drive, while demonstrating comparable performance. The hybrid hard drive is a recent innovation that utilizes a flash memory buffer to cache a user’s most frequently used files, allowing magnetic portion of the hard drive to spin down.

The national electricity savings with 719 000 volume class servers in use is about 31 GWh.

Figure 8 below illustrates potential energy efficiency improvements.


Figure 8: Latitude for Energy Efficiency Improvements

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2004 2005 2006 2007 2008 2009 2010 2011 2012

Estim

ated

ann

ual e

nerg

y us

ed /

save

d by

ser

vers

(MW

h)

Hard Drive

SPS

SecondaryPow er Supply

Virtualization

Pow er Supply

4. References

Aebischer, B., R. Frischknecht, C. Genoud, and F. Varone, Energy Efficiency Indicator for High Electric-Load Buildings, The Case of Data Centres, Proceedings of the IEECB 2002, 2nd International Conference on Improving Electricity Efficiency in Commercial Buildings, Nice, France, 2002.

ACEEE, and CECS. Funding prospectus for Analysis of Data Centers and their implications for energy demand, Washington, DC, American Council for an Energy Efficient Economy (ACEEE), Center for Energy and Climate Solutions (CECS), 2001.

Bailey, M., M. Eastwood, T Grieser, L. Borovick, V. Turner, and R.C. Gray. 2007. Special Study: Data Center of the Future. New York, NY: IDC. IDC #06C4799. April.

Brown, E., R. N. Elliott, and A. Shipley, Overview of Data Centers and Their Implications for Energy Demand. Washington, DC, American Council for an Energy Efficient Economy, Center for Energy & climate Solutions (CECS). 2001.

Carr, N. G., The End of Corporate Computing, MIT Sloan Management Review, vol. 46, no. 3, pp. 67-73, Spring, 2005.

Ecos and EPRI, Efficient Power Supplies for Data Center and Enterprise Servers, Final report, 2008, http://www.80plus.org/documents/ServerResearchReportFinal.pdf.

EPRI and Ecos, Challenges and Energy Saving Opportunities in Measuring, Reporting, and Promoting High Efficiency Secondary Power Supplies, PIER final report prepared for the California Energy Commission, 2008.

European Commission, Code of Conduct on Data Centres Version 0.7, October 2007, http://re.jrc.ec.europa.eu/energyefficiency/pdf/meeting%20Data%20Centers%20CoC%204%20December%202007/CoC%20Data%20Centres-v07-WORKING-DRAFT.pdf.

Frost and Sullivan, World UPS Markets A569-27. 238 p., 2004


Gruener, J., Building High-Performance Data Centers. Dell Magazines - Dell Power Solutions (Issue 3 Building Your Internet Data Center, 2000.

IDC Canada, Server Data Cut from Canadian Quaterly Server Tracker for Natural Resources Canada, April 17th 2008a.

IDC Canada, Server Life Expectancy Guidance as per Canadian Infrastructure Hardware Research Program, May 2008b.

Intel, Planning and Building a Data Center - Meeting the e-Business Challenge, Intel Corp, 2002.

http://www.intel.com/network/idc/doc_library/white_papers/data_center /. Aug 01, 2002.

Kawamoto, K., J. Koomey, M. Ting, B. Nordman, R.E. Brown, M. Piette, and A. Meier, Electricity used by Ofiice Equipment and Network Equipment in the U.S.: Detailed Report and Appendices. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-45917. 2001.

Koomey, G.K., O.Sezgen, and R. Steinmetz, Estimating Total Power Used by Data Centers in California, 2005, http://hightech.lbl.gov/library.html

Koomey, J., Estimating Total Power Consumption by Servers in the U.S. and the World, 2007a

Koomey, J., Estimating Regional Power Consumption by Servers: A Technical Note, 2007b.

Requires actual IDC report # - can this be provided for this reference?

Loper, J., and S. Parr, Energy Efficiency in Data Centers: A New Policy Frontier, Washington, DC: Alliance to Save Energy, 2007.

McKinsey and Company, Revolutionizing Data Center Efficiency—Key Analyses, Presentation at the 2008 Uptime Symposium, 2008, http://uptimeinstitute.org/.

Mitchell-Jackson, J., J. Koomey, M. Blazek, and B. Nordman, National and Regional Implications of Internet Data Center Growth, 2001, http://enduse.lbl.gov/Info/National_Implications.rev2.pdf

Mitchell-Jackson, J., Energy Needs in an Internat Economy: A closer Look at Data Centers, Masters thesis, 2001.

Roth, K., F. Goldstein, and J. Kleinman. Energy Consumption by Office and Telecommunications Equipment in Commercial Buildings--Volume I: Energy Consumption Baseline. Washington, D.C.: Prepared by Arthur D. Little for the U.S. Department of Energy, A.D. Little Reference no. 7289, 2002.

Schäppi, B., F. Bellosa, B. Przywara, T. Bogner, S. Weeren, A. Anglade, Energy Efficient Servers in Europe - Energy Consumption, Saving Potentials, Market Barriers and Measures Part I: Energy Consumption and Saving Potentials, 2007a, http://www.efficient-server.eu/fileadmin/docs/reports/E-Server_PartI_SavingPotentials_and_Scenarios_28112007.pdf.

Schäppi, B., F. Bellosa, B. Przywara, T. Bogner, S. Weeren, A. Anglade, Energy Efficient Servers in Europe - Energy Consumption, Saving Potentials, Market Barriers and Measures - Part II, Draft Version, 2007b, http://www.efficient-server.eu/fileadmin/docs/reports/E-Server-Report_PartII.pdf.

Schäppi, B., F. Bellosa., B. Przywara, T. Bogner, S. Weeren, A. Anglade, Energy Efficient Servers in Europe Report Part III Energy Efficiency Criteria and Benchmarks, 2007c, http://www.efficient-server.eu/fileadmin/docs/reports/E-Server-Report_Part3.pdf.

The Economist, Pocket World in Figures 2006, 2006.

TIA, Compilation of Definition of Terms, Acronyms and Abbreviations, Units of Measure, and Symbols from TR-42 and TR-41.7.2 Telecommunications Standards Modified and Accepted by the TR-42.5 Subcommittee, 2005, http://ftp.tiaonline.org/TR-42/Tr425/Public/TR425-05-10-010a_working_dictionary.pdf

Ton, M., and B. Fortenbery, Server Power Supplies, Berkeley CA: Report to Lawrence Berkeley National Laboratory by Ecos Consulting and EPRI Solutions, 2005.

Tschudi, W.F., Xu, D. Sartor, and D. Stein, High Performance Data Centers: A Research Roadmap, LBNL-53483, 2003, http://hightech.lbl.gov/library.html


Turner IV, P., J.H. Seader, and K. G. Brill, Tier Classifications Define Site Infrastructure Performance, The Uptime Institute, 2008.

U.S. Environmental Protection Agency, Report to Congress on Server and Data Center Energy Efficiency Public Law 109-431, 2007.


5. Appendices

Appendix 1: Glossary

Blade servers: Server chassis housing multiple, thin modular electronic circuit boards, known as servers blades.

CAGR (Compound annual growth rate): CAGR is an average annual growth rate over a period of several years.

Comatose servers: Comatose servers are those that run applications no longer needed or run no applications at all yet remain installed and operating continuously.

High-end servers: Servers with an average selling value above $500 000.

Mid-range servers: Server with an average selling value between $25 000 and $499 000.

Non-x86 servers: Non-x86 servers include a wide range of server architectures and software platforms designed to perform a particular task-specific function, usually handling many instructions at the same time.

Rack-optimized servers: Servers designed to be installed in a framework called a rack.

Sunk cost: In economics and in business decision-making, sunk costs are costs that have been incurred and which cannot be recovered to any significant degree.

Virtualization: Virtualization technology allows multiple, software based “virtual machines” to run on a single server, allowing that server to run 20 times the number of application.

Volume servers: Server with an average selling value below $25 000.

Workload: In computing, the workload is the amount of processing that the computer has been given to do at a given time. The workload consists of some amount of application programming running in the computer and usually some number of users connected to and interacting with the computer's applications.

x86 servers: x86 refers to the most commercially successful instruction set architecture in the history of personal computing.

.


Appendix 2: Estimating Energy Use by Servers in Canada – Previous Works

There are no publicly available server energy use estimates for Canada. Ecos’ first analysis of the Canadian server market on behalf of NRCan included estimates of Canadian installed base of servers and energy use by servers. The results of this first analysis were discussed at the Energy Efficient Data Centres Executive Round Table in Ottawa, 24 January 2008.

For this preliminary analysis, Canadian estimates of the number of servers installed in Canada were derived using a top-down approach from Koomey’s 2007 research, which provides worldwide and U.S. installed base estimates for servers (Koomey, 2007a), as shown in Figure 1. We assumed that Canada represents about 8 percent of the U.S. server market in terms of shipments, or about the same as the ratio of the Canadian and the U.S. economies in terms of gross domestic product.

A2-Figure 1: Phase I – Ecos’ Energy Scaling Methodology

10 306 thousand

units

2005 United States Server Stock

= about8%×

Canadian % of

United States

Servers Market826

thousand units

2005 Canadian

Server Stock

10 306 thousand

units

2005 United States Server Stock

= about8%×

Canadian % of

United States

Servers Market826

thousand units

2005 Canadian

Server Stock

826 thousand

units

2005 Canadian

Server Stock

Source: 2005 U.S. server Installed base comes from Koomey (2007a). Canadian percent of U.S. server market is based on Ecos’ analysis of the Canadian server market (Ecos, 2007).

Other factors considered included the ratio of the Canadian and U.S. population and the ratio of the number of “secure servers”11 in Canada and in the U.S. (see A2-Table 1, below).

A2-Table 1: Scaling Factors Considered for Canadian Server Market Analysis

Canada U.S. Scaling Factor (U.S. to Canada)

GDP (trillion U.S. $) 0.856 10.9 8% Population 32 976 000 303 284 578 11% Number of secure servers (Frost & Sullivan, 2004) 6 841 187 673 4% Sources: The Economist, 2006.

11 A secure server is a Web server that supports any of the major security protocols, like SSL, that encrypt and decrypt messages to protect them against third-party tampering (Frost & Sullivan, 2004).


On a population basis, Canada would account for about 11 percent of the U.S. shipment of products. On the basis of the number of secure servers, Canada would account for about 4 percent of the U.S. shipment of products (Frost & Sullivan, 2004). Another factor used to validate this 8 percent estimate was that it fell neatly between the 4 percent number of secure servers and the 11 percent population ratio. Following this methodology, we estimated the installed base of servers in Canada at 826 000 units. A2-Table 1 compares Ecos’ estimate of the number of servers in Canada with Koomey’s estimates of the number of servers in the U.S. and worldwide in 2005. As indicated in A2-Table 1, volume servers represented over 95 percent of the market in terms of units.

We used Koomey’s U.S. weighted average power use per unit for each server class as estimates of power use per unit for each server in Canada (Koomey, 2007a). The product of the estimated installed base by class and the average power use per server class yields the electricity used for that server summarizes the installed base, average power use and electricity consumption by server class for Canada, the U.S. and the world. In 2005, total electricity consumption for all servers in Canada was about 1.81 billion kWh.

A2-Table 2: Estimated 2005 Server Installations, Power Use and Energy Use

Region Server class Installed units (thousands)

Average power use per server (watts)

Total electricity consumption (billion kWh/yr)

Canada Volume 792 219 1.52 Mid-range 32 625 0.17 High-end 2 7 651 0.12 Total 826 - 1.81

U.S. Volume 9 897 219 19 Mid-range 387 625 2.1 High-end 22 7 651 1.5 Total 10 306 - 22.6

World Volume 25 959 222 50.5 Mid-range 1 264 607 6.7 High-end 59 8 106 4.2 Total 27 282 - 61.45

Sources: Koomey 2007a for installed base of servers in the U.S. and in the world, average power use per server, and total electricity consumption of servers in the U.S and in the world. The Canadian server installed base numbers were derived form Koomey 2007a assuming that Canada represents about 8 percent of the U.S. server market.

Figure 2, below, shows the Canadian energy consumption by server class for 2000 and 2005, including cooling and auxiliary equipment. As Koomey (2007a), we multiplied the total electricity consumption by servers by a factor of two to get the total electricity consumption including electricity use for cooling and auxiliary equipment. As more detailed data become available, this hypothesis should be tested for Canada.


A2-Figure 2: Estimated Allocation of Server Energy Consumption in Canada, 2000 and 2005

0

1

2

3

4B

illio

n kW

h/ye

ar

Cooling and auxillary equipment

High-end servers

Mid-range servers

Volume servers

Sources: U.S. energy consumption estimates come from Koomey (2007a). Canadian energy consumption by server class comes from Ecos’ analysis of Koomey’s U.S. data assuming that Canada represents about 8 percent of the U.S. server market. As Koomey (2007a), we multiplied the total electricity consumption by servers by a factor of two to get the total electricity consumption including electricity use for cooling and auxiliary equipment.

Aggregate electricity use for servers doubled over the period 2000 to 2005. Most of this growth was the result of growth in the number of volume servers.

The findings of our first analysis are based on best estimates from a variety of sources. Following this first analysis, Ecos recommended that NRCan purchase data from IDC Canada to support this market analysis.

2000 2005


Appendix 3: IDC Canada Data Source The server data cut provided by IDC Canada came from three main sources:

IDC’s Canadian Quarterly Server Tracker

IDC Canada tracks Canadian server shipments into the market on a quarterly basis as part of its Canadian Quarterly Server Tracker service. Data inputs to this service include quarterly server manufacturer and their channels shipment data as well as demand side research conducted by IDC.

IDC’s Canadian Server Tracker, Forecast View

Based on ongoing observation of server shipments and related trends through the Canadian Quarterly Server Tracker , along with primary research conducted with IT managers in Canadian business as part of the Canadian Infrastructure Hardware research program, IDC generated the Server Tracker Forecast View worksheet that was provided. IDC also provides this forecast to its server manufacturer clients.

IDC’s Canadian Server Workloads 2004 Study

The Server Workloads 2004 data provided by IDC Canada is summary data collected as part of a syndicated study conducted by IDC in 2004. The study's goal was to help the sponsors of the study (server manufacturers) better understand the business needs that drive server deployment.

Guidance on Canadian server life expectancy was provided by IDC Canada from research conducted within its Canadian Infrastructure Hardware research program

http://idc.com/getdoc.jsp?containerId=IDC_P2022�




Appendix 4: Workload Taxonomy (IDC Canada, 2008a)

Workload

BUSINESS PROCESSING

ERP. Includes the following transactional applications: Enterprise wide line-of-business applications (e.g., Oracle, PeopleSoft and SAP) Other business commerce applications that facilitate business transactions or other task automation over networks Departmental transactional applications that run on servers but don't tie directly to other applications CRM. Automates the customer-facing business processes within an organization: sales, marketing, and customer service (Collectively, these applications serve to manage the entire life cycle of a customer in helping an organization to build and maintain successful relationships.) OLTP. Uses a database; is not ERP Batch. Traditional legacy mainframe-type processes that execute business process transitions in a batch process

DECISION SUPPORT Data warehousing/data mart. Tools that are used to create and run data warehouses and data marts Data analysis/data mining. Tools used to access data warehouses: online analytical processing (OLAP), data mining, data visualization, Web query tools, and so on

COLLABORATIVE Email. Traditional email applications Workgroup. Applications that let users collaborate and share information, such as groupware applications

APPLICATION/SOFTWARE

AADEVELOPMENT Traditional application development work

IT INFRASTRUCTURE

File/print sharing. Includes infrastructure applications whose purpose is primarily as an endpoint (A transaction is sent to the network to be printed or stored on a disk, or a request is made get information from the network. This category also would include cache servers) Networking. Includes the following networking applications: directory, security/authentication, network data/file transfer, communication, and system data/file transfer Proxy caching. Includes applications that improve data centre performance by storing and serving content from the edge of the network Security. Includes applications specifically designed for authentication and identification and typically performs "firewall" services Systems management. Includes applications that monitor and account for systems performance, resource planning, and resource allocation

WEB INFRASTRUCTURE Web serving. Utilizes HTTP protocols to accept requests from other servers and then search file systems according to the request Streaming media. Video and audio multimedia applications for streaming, including an Internet component

TECHNICAL This definition of technical workloads does not include infrastructure activities, which are included as part of a primary usage model of scientific computing. Note: This document uses the terms scientific/engineering and technical interchangeably.

OTHER This category includes any other application types not covered by the above definitions.


Appendix 5: CPU Type Taxonomy (IDC Canada, 2008a)

Complex instruction set computer (CISC). This design is the traditional type of computer processor. CISCs have large instruction sets with simple and complex instructions of variable lengths. Although x86 chip types (such as those produced by Intel and AMD) are CISC processor designs, they are separated into their own chip-type categories because of their high volume. In IDC's server taxonomy, CISC-based systems refer to proprietary systems, such as those produced by IBM for its zSeries servers.

Reduced instruction set computer (RISC). This processor design is produced by IBM, HP, Sun Microsystems, and others. RISCs have smaller instruction sets, and each instruction is usually of limited function with fixed-length formats. RISC servers typically support Unix and other (e.g., Tandem) platform software. Sun's SPARC, HP's PA-RISC, and IBM's POWER processors are all examples of RISC architecture.

Explicitly Parallel Instruction Computing (EPIC). This processor design is produced by Intel and represents its 64-bit Itanium processor family (codeveloped by Intel and HP). Several server OEMs have products that utilize the 64-bit Itanium processor.

x86. What was once referred to as the Standard Intel Architecture Server (SIAS) market will now encompass all x86(32)- and x86(64)-based systems, and will be referred to as the x86 server market in IDC publications and databases. The x86 server market includes all systems that fit IDC chip-type definitions, regardless of form factor (such as blades) and regardless of price band or CPU capacity (i.e., systems with price points above $25,000 and/or containing more than eight processors will still be in the x86 server category as long as they meet chip-type definitions):

x86(64). This processor design refers to x86 architecture systems that have 64-bit extensions. x86(64) processor designs enable 64-bit computing while remaining compatible with existing x86 software infrastructure. AMD's Opteron processor and Intel's EM64T are examples of x86(64) processors.

x86(32). x86(32) refers to volume 32-bit CISC processors developed and produced by companies such as Intel and AMD. Intel's Pentium and XEON processor families and AMD's Athlon processors are examples of this architecture. x86(32)-based systems run Microsoft Windows, Novell NetWare, Linux, Unix, and other operating system environments


Appendix 6: Calculation for % of Replacement and Average Age of Installed Base of Servers in 2007

Replacement vs. New Sales

The difference between the installed base of servers in 2007 and the installed base of servers in 2006 equals the new server capacity:

020304050606 *49.0 salessalessalessalessalesaseinstalledb ++++=

030405060707 *49.0 salessalessalessalessalesaseinstalledb ++++=

0203070607 *49.0*51.0 salessalessalesaseinstalledbaseinstalledb −−=−

We estimated the installed base of servers using data on shipments provided by IDC Canada (2008a). We calculated that the average server life expectancy is 4.49 years. The average server life expectancy was calculated by using a weighted average of the x86 and non-x86 server life expectancies provided by IDC (IDC, 2008b).

The % of new sales in 2007 is equal to:

2007

20062007

salesaseinstalledb −∆

% of replacement = 1-% of new sales

Average age of the server installed base in 2007

The average age of servers was calculated based on a weighted average of the age of servers which compose the 2007 installed base of servers; see the following equation:

07

030405060707

)25.4()2/1()5.3()5.2()5.1()2/1(

endend aseinstalledb

salessalessalessalessalesAverageAge

++++=


Appendix 7: Ecos Estimates of Canadian Shipment Data for 2000 and 2001 A7-Table 1: Koomey’s World Server Installed Base Information (Koomey, 2007a)

A7-Table 2: Ecos Canadian Percentage of the World Shipment (2002)

A7-Table 3: Ecos Canadian Shipments for 2000 and 2001

2000 2001 2002 Volume 3 926 000 3 981 000 4 184 000 Mid-range 283 000 206 000 204 000 High-End 13 000 10 400 9 400 Total 4 222 000 4 197 400 4 397 400

Canada

Percentage (2002)

Volume 2.6% Mid-range 3.4% High-End 4.1%

2000 2001

Volume 101 960 103 388 Mid-range 9 725 7 079 High-End 531 425


Appendix 8: Energy Analysis, 2008

Units Volume Mid-range High-end Installed base

Canada 753 841 22 541 1 033 Newfoundland and Labrador 11 973 358 16

Prince Edward Island 3 219 96 4 Nova Scotia 21 834 653 30

New Brunswick 17 512 524 24 Quebec 177 236 5 300 243 Ontario 292 734 8 753 401

Manitoba 27 393 819 38 Saskatchewan 23 160 693 32

Alberta 76 921 2 300 105 British Columbia 99 443 2 974 136 Yukon Territory 722 22 1

Northwest Territories 992 30 1 Nunavut 701 21 1

Average power used per server Watts/server 224 598 8 378 Annual energy consumption per server

kWh/server 1 963 5 241 73 433 Direct electricity consumption

Canada GWh/year 1 480 061 118 150 75 847 Newfoundland and Labrador GWh/year 23 508 1 877 1 205

Prince Edward Island GWh/year 6 320 505 324 Nova Scotia GWh/year 42 867 3 422 2 197

New Brunswick GWh/year 34 383 2 745 1 762 Quebec GWh/year 347 979 27 778 17 833 Ontario GWh/year 574 742 45 880 29 453

Manitoba GWh/year 53 782 4 293 2 756 Saskatchewan GWh/year 45 471 3 630 2 330

Alberta GWh/year 151 023 12 056 7 739 British Columbia GWh/year 195 242 15 586 10 005 Yukon Territory GWh/year 1 418 113 73

Northwest Territories GWh/year 1 948 156 100 Nunavut GWh/year 1 376 110 71

(1) The installed base for Canada was calculated by adding the server shipments over the useful life of servers. We used information on population size and distribution by province to split the Canadian shipments by province/region. (2) The average power used by server class for Canada was taken from Koomey 2007b. We used the average power used for the rest of the world in 2005 directly from Koomey (2007b). (3) Annual energy consumption per server was calculated by multiplying the average power usage by the number of hours in a calendar year. We assumed 8765 hours/year and 100% load factor/ (4) Direct electricity consumption was calculated by multiplying the server installed base by province by the average energy consumption per server.

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