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Report: Reducing the Carbon Footprint and Saving Energy
Carbon Footprint refers to the amount of energy lost as carbon pollution into the environment. Life Cycle Assessments (LCA) are carried to learn the amount of carbon footprint of a process from the raw materials to the end-of-use. International and national standards and regulations are made for all industrial sectors, because gases with carbon, like methane and carbon dioxide (CO2), are greenhouse gases (Van Landegem 3). The greenhouse effect holds heat next to the surface of the earth making global warming worse. The measure of LCA is the carbon footprint that is reported in carbon dioxide or carbon equivalents (CO2e) (Stephens). The LCA model is used because a positive change the LCA value means a carbon footprint reduction and an increase in energy efficiency (Telstar 3).Therefore, reducing the greenhouse effect is one reason to reduce the energy we use at Ericcson Canada.
The negative environmental impact of processes is found by calculating CO2e and reported as the carbon footprint (Stephens). Many global institutions like the United Nations set standards for sustainability and the carbon footprint calculated with the LCA. The reduction of CO22 is required for all industrial sectors. The ICT industry is unique because as usage grows the amount of energy efficiency can be increased at the same time by applying recommendations by Green Software Engineers. The field of Green Software Engineering is focused on developing strategies to measure energy efficiency and the carbon footprint of software (Kern, Dick, Naumann, and Hiller).
The Ericsson Improvement Management Process is designed with six steps carried out in the following order define, measure, analyze, improve and control (Ericssona 16). Fortunately, using less energy also saves costs on utilities for the company. The main activities that cause the ICT carbon footprint are the ICT infrastructure, the office space and the employees including their commuting to work (Kern et al. 3).
The purpose of this report is to propose a project to reduce the carbon footprint at Ericsson Canada. The following report discusses the expected increase in ICT use until 2020. A plan is recommended to save energy by improving energy efficiency. A way to calculate the amount of efficiency and a table to compare the results to other centers are presented. Results are discussed. Recommendations are made in the conclusions.
Figure 1 Carbon footprint 2020 CT sector (Van Landegem 3); Potential increase in data traffic until 2020 (2)
A huge amount of data traffic growth in North America is expected by the year 2020 as more and more people gain access to broadband. Personal computers (PCs), peripherals and printers emitted 5.7 percent of the total 1.43 billion tonnes CO2 equivalent emissions (3). (See fig. 1) Telecoms’ infrastructure emitted 25 percent and 18 percent from data centers. (See fig. 1) In North America the data traffic will potentially increase, exponentially. (See fig. 1 right) The increases in the data traffic will highly influence data centers’ energy usage. Globally, the data centers were found to emit approximately 25 percent of the manmade CO2 emissions (van Heddeghem, Lambert, Lannoo, and Colle et al.).
Figure 2 Goal for reduction in kg CO2e 1995 to 2020 (Ericssonb 2013)
The 2020 global target for ICT CO2e emissions reduction by Ericsson is to equal the amount of 80 kg CO2e ICT total carbon per average user with the data center emissions included. In 1995, the amount was 300 kg CO2e ICT total carbon per average user, that amount was reduced to 100 kg CO2e total carbon per average user by 2007 so successful strategies can be applied.
Green software engineering divided the ICT infrastructure into its several components that are involved in increasing the CO2 emissions. The components are backup storage systems, workstations, PCs, rack mount servers, offering an uninterrupted power supply and server administration that must run 24 hours a day, every day (3). Data centers offer an opportunity to reduce energy consumption, decrease energy costs and cut CO2 emissions (2). I propose that Ericsson Canada redesign the layout for the data center to become more energy efficient. The project for data centers can reduce the CO2 equivalent emissions and save money (3). A comparison between data centers, desktop PCs and savings potential of servers compared to d
The results of the calculations can be compared to the values in table 1 in order to learn if the efficiency is average to best-in-class. The table shows the measurements taken in at the Uptime Institute Uptime Tier II, Uptime III, and Internet IV in 2007. The table also shows the measurements take for annual kilowatt hour, the costs in annual $0.10 per kilowatt hour and MTons of CO2e resulting from the reported values for PUE and DCIE. The ‘best-in-class’ rank In order to reach an average ranking in the Internet Tier II when PUE is equal to 2.00 and DCIE is equal to 50 percent.(See table 1) In order to reach best-in-class, PUE needs to equal 1.21 and DCIE needs to equal 89 percent. (See table 1)
The Total Facility Power (PUE) is a measure of the energy efficiency of the IT equipment compared to the total facility power.
PUE = (Total facility Power) / IT Equipment Power Eqn. 1
The Data Center Infrastructure Efficiency (DCIE) reports the efficiency in percentage. The equation for DCIE is written below.
DCIE = 1/PUE x 100% Eqn. 2
Smart meters need to be installed where ever they are needed so the Power Usage
Figure 3 Green Grid schematic of data center metrics (Ulman 3)
Figure 3 is an example showing that when PUE = 2.5, then DCIE = 40 percent (3). The calculation is shown below.
PUE = 1000 KW total power in / 400 KW used by IT = 2.5 = average Eqn. 3
DCIE = 1/ (2.5) x 100% = 40% Eqn. 4
Analysis of results
The analysis started by assuming that the kilowatt (KW) power (energy) input into the IT infrastructure was equal to 1000 KW. The amount of energy input is kept constant at 1000 KW and the amount of KW used by the ICT date center. The chart can be used to learn how much KWs used by the IT to reach the goals in ranking for ‘average,’ ‘good,’ ‘very good’ and ‘best-in-class.’ The ultimate goal for placing all the servers in one location is to reach a ranking of ‘best-in-class’.
The base amount that was used for the calculations was to assume an energy input of 1000 KW and 400 KW used by the data servers shown in the example above. The range of KW hours calculated was from 200 to 900 KW. The more servers that are placed in one place, the hire the energy needs, but by adjusting the PUE and DCIE the optimum amount can be calculated. The base amount resulted in a PUE of 2.5 and 40 percent DCIE and so is equal to an average rating. If the KW used by the ICT is reduced by some means such as passive cooling the 325 KW used by IT is a PUE of 3.1 and a DCIE of 32.5 percent for another average rating. (See table 1 & 3)
But, the strategy I am recommending is to consolidate all the servers together in one location. In that case, the KW will increase as the number of data servers increase. At 425 KW the PUE is 2.4 and the DCIE is 42.5, giving an ‘average’ rating according to table 1. A ‘good’ ranking is reached at 500 KW with a PUE of 2.0 and a DCIE of 50 percent. (See tables 1 & 3) A ‘very good’ rating is reached at 850 KW when the PUE equals 1.2 and the DCIE equals 85 percent. (See tables 1 & 3) Finally, a ‘best-in-class’ is when the input energy is 875 KW and the PUE is 1.1 and the DCIE is 87.5 percent. (See tables 1 & 3)
Conclusions and recommendations
Only a few steps are necessary to improve energy efficiency, but the steps need to be taken while taking careful measurements in order to meet the optimum designs. The optimum amount of energy efficiency is correlated directly with the reduction in CO2e. Firstly, servers need to be consolidated into one location. Servers that are now operating independently and multiple serves in many rooms need to be one room for improved energy efficiency. The potential improvement if the data servers are consolidated in one place ranges from ten percent to 40 percent (Energy Star 1). Arrange the servers so that the fronts face each other and the backs of the servers are face to face (2). And then divide the areas with cold air mixing from the hot exhaust by installing curtains made with flexible strips (2). The changes will amount to the optimum amount of CO2e, but measurements need to be continually taken. The measurements will allow inefficiencies like heat loss to be identified so that improvements can be made.
The measurements that need to be taken throughout each day can be used to compute end-of-year measurements for annual kilowatt hour, the costs in annual $0.10 per kilowatt hour and MTons of CO2e resulting from the calculated reported values for PUE and DCIE. The bottom line is to reach a reduction in CO2e even while the data traffic is increasing.
The carbon footprint of new technology that improves ICT but uses more energy needs to be taken into account too. High energy needs technology being introduced and impacting data centers include servers that are ultra-fast, cloud computing, and new networks for telecommunications. Cooling devices and air conditioning use high amounts of energy so changing the configuration of the servers in face-to-face and back-to-back can work to causes lowering of energy loss without having to add devices or air condition the space. Data servers are going to be very busy because so many government and business sites are going to move from single to multiple desktop computers, more powerful modems are becoming available and at least one mobile phone per person are owned even in developing countries (Dunn 1).
All the servers need to be assessed at Ericsson Canada. Take the opportunity to identify the servers that are hooked up to electricity, but are not used for computing. Energy Star estimates that 15 to 30 percent of data center equipment is not used for computing (1).
All servers need to be moved to one central location.
The data servers that are not being used can be moved to the central location with the other servers and put to use as data traffic increases.
Next, the servers that are not used very often also need to be consolidated and used to handle the increased data traffic for a savings of five percent to 15 percent (1).
The servers at the central location must be placed s that face-to-face and end-to-end to take advantage of a passive cooing technique.
Divide the area holding the servers between the regions with cold air mixing and the hot exhaust by installing curtains made with flexible strips
Install smart meters so that energy use measurements in KW hours are taken regularly, consistently and reliably.
Dunn, H.S. The carbon footprint of ICTs. Global Information Society Watch, 1-2, 2010. http://www.giswatch.org/thematic-report/sustainability-climate-change/carbon-footprint-icts
Energy Star. Top 12 ways to decrease the energy consumption of your data. (n.d.) http://www.energystar.gov/ia/products/power_mgt/downloads/DataCenter-Top12-Brochure-Final.pdf?0eca-8a40
Ericssona. How we manage our business. Ericsson Operational Quality Manual, Ericsson, January 2012. http://www.ericsson.com/res/thecompany/docs/comp_facts/how-we-manage-our%20business.pdf
Ericssonb. Combining technology and services to embrace change. 2014. http://www.ericsson.com/ourportfolio
Kern, E., Dick, M., Naumann, S. & Hiller, T. Impacts of software and its engineering on the carbon footprint of ICT. Environmental Impact Assessment Review, 19 August 2014, 1-9 http://www.sciencedirect.com/science/article/pii/S0195925514000687
Stephens, A. GHG protocol product standard ICT sector guidance. Presentation to the Digital Agenda Assembly Workshop 11, Greening ICT, 16 June 2011. http://ec.europa.eu/digital-agenda/sites/digital-agenda/files/5.pdf
Telstra. Towards a high-bandwidth, low-carbon future: Telecommunications-based opportunities to reduce greenhouse gas emissions. Telstra Corporation, Ltd, pp. 1-8 2007. http://www.telstra.com.au/abouttelstra/download/document/telecommunications-climate-change-blueprint-in-brief.pdf
Uhlman, K. Unleashing stranded power and cooling from new and existing data centers. White Paper 10-03. Eaton, April 2010. http://www.eaton.com/powerquality
Van Heddeghem, W., Vereecken, W., Colle, D., Pickavet, M. & Demeester, P. Distributed computing for carbon footprint reduction by exploiting low-footprint energy availability. Future Generation Computer Systems, 28, 405-414, 2012. http://www.sciencedirect.com/science/article/pii/S0167739X11000859
Van Landegem, T. Reducing the carbon footprint of ICT devices, platforms and networks. GreenTouch, GeSI Global Assembly, May 2012. http://www.greentouch.org
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