Energy Efficient Data Center
source : http://hightech.lbl.gov/presentations/6-23-05_PGE_Workshop.ppt&ei=BVxPVIy_Bse68gWwy4HAAw&usg=AFQjCNGHU_rSwcF4BMo2A6KnFfSZglP2UA&sig2=wZlTGXORD_HOUDJi-a2uAA&bvm=bv.77880786,d.dGc
DC Modular Datacenter for Improved Energy EfficiencyJerry Sheehan
The GreenLight project aims to construct a modular datacenter to improve energy efficiency. It will instrument the datacenter to closely monitor temperature, power utilization, and other metrics. Researchers will populate it with different computing architectures and investigate techniques for optimizing energy usage. The project also plans to directly power the datacenter with DC electricity from solar and fuel cell generators to avoid conversion losses.
This document summarizes a study on improving energy efficiency in data centers through a cyber-physical approach combining hardware and software monitoring. The study developed an optimization framework that gathers data on environmental, server, and workload parameters in real-time to dynamically adapt and propose optimizations. It deployed a wireless sensor network in a supercomputer data center to monitor inlet/outlet temperatures and other environmental data at adjustable sampling rates, reducing the amount of collected data by up to 68% while still capturing useful information. The approach was tested in a real case study to holistically optimize energy use through integrated IT and cooling system management.
This document describes an interactive "Kiosk Mode" for visualizing and analyzing solar PV power and weather data collected from sites around the world. It collects nearly 1TB of data from various solar farms and analyzes the data using R. Examples of analyses include wind gust analysis of a rooftop site and monthly power production trends. The document outlines the architecture of the Kiosk Mode, which allows users to select, plot, and download data. Future work is aimed at hosting it online and improving data ingestion and the user interface.
This document discusses using microinverter data from Enphase systems to characterize photovoltaic modules and identify underperformance. Microinverter data provides detailed performance data at the individual module level. The approach infers site orientation metadata like tilt and azimuth by correlating current output with clear sky irradiance models. It also infers module metadata like voltage and current references by analyzing output trends. Together this allows visualization of module performance over time and identification of issues. The analysis does not require external sensors or site visits, using only the microinverter data, with the goal of automatically monitoring many systems.
Commercial Overview DC Session 3 The Greening Of The Data Centrepaul_mathews
Data centers consume large amounts of electricity and produce significant carbon emissions. They are often inefficient, with only around 50% of energy going to IT loads while the rest is lost to physical infrastructure. This can be improved through better sizing, modular scalable designs, high-efficiency equipment, and optimization techniques like hot/cold aisle containment. Achieving higher data center infrastructure efficiency (DCiE) through monitoring and improvement strategies can reduce electricity bills by up to 50% and lower environmental impact.
Building a next generation data center presents many challenges related to power, cooling, physical layout, security, and safety. Moore's Law dictates that computing power will continue to rapidly increase, driving up power and cooling demands. Existing data centers often operate near or over capacity for room temperature, UPS power, and cable management. The next generation design must have scalable facilities, efficient 220V power distribution, proper cooling of equipment rather than the entire room, and comprehensive documentation to ensure reliability, disaster recovery, and energy efficiency.
- NREL is researching complex flow within wind plants using high-performance simulations (SOWFA) to improve industry tools and understanding.
- Current wind plant models lack full physics and have high uncertainty, leading to underperformance predictions and unreliable structural load estimates.
- Research aims to capture effects of complex terrain, atmospheric stability, and plant layout on power production and fatigue loads through detailed field measurements and advanced simulations.
- Improved wind plant modeling and controls that react to and modify complex flow could increase power output by over 40% and better predict structural loads.
This presentation discusses how to optimize data center efficiency. It begins with an introduction of new regulations for energy efficiency in Europe. Then it discusses basics of data center design including power, cooling, and availability requirements. It notes that energy costs are rising significantly. The presentation explores optimizing hardware, UPS systems, and cooling to improve efficiency. It provides examples of efficiency gains from right-sizing infrastructure and high-efficiency UPS systems. Overall, the presentation suggests that data center efficiency can be improved by up to 40% through optimization techniques.
DC Modular Datacenter for Improved Energy EfficiencyJerry Sheehan
The GreenLight project aims to construct a modular datacenter to improve energy efficiency. It will instrument the datacenter to closely monitor temperature, power utilization, and other metrics. Researchers will populate it with different computing architectures and investigate techniques for optimizing energy usage. The project also plans to directly power the datacenter with DC electricity from solar and fuel cell generators to avoid conversion losses.
This document summarizes a study on improving energy efficiency in data centers through a cyber-physical approach combining hardware and software monitoring. The study developed an optimization framework that gathers data on environmental, server, and workload parameters in real-time to dynamically adapt and propose optimizations. It deployed a wireless sensor network in a supercomputer data center to monitor inlet/outlet temperatures and other environmental data at adjustable sampling rates, reducing the amount of collected data by up to 68% while still capturing useful information. The approach was tested in a real case study to holistically optimize energy use through integrated IT and cooling system management.
This document describes an interactive "Kiosk Mode" for visualizing and analyzing solar PV power and weather data collected from sites around the world. It collects nearly 1TB of data from various solar farms and analyzes the data using R. Examples of analyses include wind gust analysis of a rooftop site and monthly power production trends. The document outlines the architecture of the Kiosk Mode, which allows users to select, plot, and download data. Future work is aimed at hosting it online and improving data ingestion and the user interface.
This document discusses using microinverter data from Enphase systems to characterize photovoltaic modules and identify underperformance. Microinverter data provides detailed performance data at the individual module level. The approach infers site orientation metadata like tilt and azimuth by correlating current output with clear sky irradiance models. It also infers module metadata like voltage and current references by analyzing output trends. Together this allows visualization of module performance over time and identification of issues. The analysis does not require external sensors or site visits, using only the microinverter data, with the goal of automatically monitoring many systems.
Commercial Overview DC Session 3 The Greening Of The Data Centrepaul_mathews
Data centers consume large amounts of electricity and produce significant carbon emissions. They are often inefficient, with only around 50% of energy going to IT loads while the rest is lost to physical infrastructure. This can be improved through better sizing, modular scalable designs, high-efficiency equipment, and optimization techniques like hot/cold aisle containment. Achieving higher data center infrastructure efficiency (DCiE) through monitoring and improvement strategies can reduce electricity bills by up to 50% and lower environmental impact.
Building a next generation data center presents many challenges related to power, cooling, physical layout, security, and safety. Moore's Law dictates that computing power will continue to rapidly increase, driving up power and cooling demands. Existing data centers often operate near or over capacity for room temperature, UPS power, and cable management. The next generation design must have scalable facilities, efficient 220V power distribution, proper cooling of equipment rather than the entire room, and comprehensive documentation to ensure reliability, disaster recovery, and energy efficiency.
- NREL is researching complex flow within wind plants using high-performance simulations (SOWFA) to improve industry tools and understanding.
- Current wind plant models lack full physics and have high uncertainty, leading to underperformance predictions and unreliable structural load estimates.
- Research aims to capture effects of complex terrain, atmospheric stability, and plant layout on power production and fatigue loads through detailed field measurements and advanced simulations.
- Improved wind plant modeling and controls that react to and modify complex flow could increase power output by over 40% and better predict structural loads.
This presentation discusses how to optimize data center efficiency. It begins with an introduction of new regulations for energy efficiency in Europe. Then it discusses basics of data center design including power, cooling, and availability requirements. It notes that energy costs are rising significantly. The presentation explores optimizing hardware, UPS systems, and cooling to improve efficiency. It provides examples of efficiency gains from right-sizing infrastructure and high-efficiency UPS systems. Overall, the presentation suggests that data center efficiency can be improved by up to 40% through optimization techniques.
This document summarizes a presentation on wind plant reliability given at a Sandia workshop. It discusses the need to take a holistic plant-level view of wind reliability rather than focusing on individual turbines. Current challenges include incomplete understanding of complex wind flows, limited data exploitation, and modeling capabilities lagging behind. Improved engineering practices, models validated with field data, and new technologies are needed, as well as leveraging internal R&D and external collaborations. Concerted field tests and simulations validated with measurement data are key to advancing modeling capabilities and overcoming barriers to new technologies.
The Friends of NELHA presented a 3 part workshop called Energy Efficiency and Auditing Workshop in Hawaii. This slideshow presentation by Dr. Roderick Hinman is the first section which discusses what electricity is, how it is measured, and how you can measure the electrical loads of each appliance in your home to make decisions that can save on your home electric bill.
Comparisons of building energy simulation softwaresZheng Yang
This document summarizes and compares several building energy simulation programs in terms of their capabilities to couple occupancy information with HVAC energy simulation. It finds that while many programs can perform energy simulation, few systematically analyze the relationship between occupancy and HVAC energy use. It identifies gaps in research and calls for studies that incorporate occupancy data into simulation and evaluate its effects on HVAC energy consumption and the response of HVAC systems to occupancy-based controls. The document also reviews commonly used simulation programs and finds that they use different approaches to model heat transfer, load calculation, occupancy-HVAC connection, HVAC modeling, and simulation, with varying degrees of accuracy, flexibility, and user friendliness.
Practical Experiences with Smart-Homes Modeling and SimulationSimulationX
Within the next years, more homes will be equipped with smart metering devices, intelligent devices and home energy management systems (HEMS). The EMS are designed to adapt Demand Side Management (DSM) to households. The goals behind the DSM can vary within the household. It can target shaving the load peaks, minimize CO2 emissions, or minimize the overall energy bill via controlling the in-house energy supply resources and intelligent consuming devices. Thus, the EMS represents the dominant ‘smart home’.
Through this contribution, different practices of smart home modeling will be presented in which SimulationX has been integrated under different configurations, software and hardware integrations. The developed models represent the state-of-the art of the current, upcoming and futuristic smart homes. The incentives behind developing these models will be deliberated, along with the economic advantages in its applications within the smart grid. Moreover, the experience behind using SimulationX for evaluating such models will be presented.
This document discusses various technologies for enabling demand response and reducing kW usage. It describes energy efficiency technologies, information and reporting systems, direct load control, distributed generation, energy management systems, smart load control systems, lighting technologies, and load shifting technologies that can be used to reduce demand. It also discusses considerations for determining the kW impacts and effects on facility operations when implementing demand reduction strategies.
This document presents a two-part study on temperature coefficients and thermal uniformity of PV modules and plants:
Part 1 examines the thermal uniformity of cells within modules and modules within PV plants through temperature mapping. It finds frame insulation reduces intra-module temperature differences the most. Backsheets with aluminum covers experience higher operating temperatures.
Part 2 determines climate-specific thermal model coefficients for PVsyst in Phoenix, Arizona based on a year of module performance data. The coefficients vary by technology and mounting, with polymer modules having higher coefficients than glass modules. For c-Si in Phoenix, the recommended coefficients are Uc=25.46 W/m2K and Uv=4.31 W/m3K
This document outlines a 4-step approach to reducing electricity costs through power monitoring. Step 1 is to monitor existing equipment usage to identify major power consumers. Step 2 determines cost-effective upgrade options using measured power costs. Step 3 seeks projects with the best return on investment, including utility incentives. Step 4 monitors post-project usage to validate savings and collect incentives. Real-world examples from manufacturing plants found potential savings of over $35,000 annually from optimizing air compressor usage. Monitoring a vacuum furnace also revealed $12,000 in annual savings from using older, less power-hungry equipment.
The document discusses NREL's wind energy systems engineering initiative, which aims to develop an integrated software framework for modeling wind plant performance and costs from a systems perspective. The framework integrates various turbine and plant models from NREL's WISDEM suite using OpenMDAO. Initial analyses demonstrate the impact of system-level coupling on sensitivity and optimization studies compared to previous simplified models. The framework allows flexible configuration and improvement of models to better capture interactions across the full wind energy system over its lifetime.
The document discusses improving data center energy efficiency (PUE) through implementing appropriate technologies. It explains key PUE concepts like shared infrastructure allocation and redundancy impacts. Realistic PUE expectations are 1.1-1.2 for ordinary data centers, while some report extreme low PUE through atypical strategies. Standardizing PUE calculations is important for valid comparisons between data centers.
Holistic District Heating Grid Design with SimulationX & Green CitySimulationX
Buildings are central elements of future smart grids. Heating and cooling demand are predictable within reason, building mass as well as heating and hot water systems provide inherent storage capacity. Additionally, the fluctuation between peak and average power of a building is much more friendly to the grid than of other network nodes like wind power or electric mobility.
A local heating grid partially supplied by renewable solar heat is currently being built in a town in Bavaria. Heat pump systems provide additional storage capacity for electric grid surplus while they serve as wind energy dump for the local utility company. Cogeneration plants and peak-power boilers provide heat and power in times of low energy coverage. The low temperature heating grid supplies decentral heat pumps, which provide required heat at a much higher temperature level to each building.
The paper describes basic modeling aspects for district heating grids with SimulationX & Green City. An interesting solar-aided grid example helps to identify benefits of a new modeling approach.
Wind power forecasting an application of machineJawad Khan
The advancement in renewable energy sector being the focus of research these days, a novel neuro evolutionary technique is proposed for modeling wind power forecasters.
The work uses the robust technique of
Cartesian Genetic Programming to evolve ANN
for development of forecasting models.
These Models predicts power generation of a wind based power plant from a single hour up to a year - taking a big lead over other proposed models by reducing its MAPE to minimum values for a single day hourly prediction.
Results when compared with other models in the literature demonstrated that the proposed models are among the best estimators of wind based power generation plants proposed to date.
This document summarizes recent and planned improvements to the System Advisor Model (SAM), a free software tool that estimates the cost and performance of renewable energy systems. SAM models photovoltaics, batteries, concentrating solar power, wind, geothermal, and biomass technologies. Recent updates include improved inverter and transformer loss models for PV, 3D shading capabilities, and expanded battery modeling options. Planned additions are a photovoltaic reliability model, mobile SDKs, and open sourcing the SAM code over the summer.
- NREL is a national laboratory operated by the U.S. Department of Energy that focuses on energy efficiency and renewable energy.
- The presentation introduces EDAPT and OpenStudio, which were created to lower the administrative costs and make energy analysis cheaper for Energy Design Assistance programs.
- EDAPT is a web service that tracks project status, manages data and communications, and reports program-wide outcomes. It integrates with OpenStudio and EnergyPlus to automate energy modeling and analysis.
The document discusses different electrical machines used for wind power generation. It begins with an introduction to wind power production and then describes various electrical generation schemes currently in use worldwide. These include constant speed wind turbines connected directly to the grid, variable speed turbines with a doubly-fed induction generator, and direct drive turbines with a synchronous or permanent magnet synchronous generator. The permanent magnet synchronous generator system directly connected to the grid via a full-scale power converter is highlighted as one of the most promising designs currently used due to its high efficiency and robustness.
More Reliable Wind Power Forecasting - OSIsoft Users ConferenceGregg Le Blanc
The document discusses WINData's plan to reduce wind forecasting uncertainty using real-time meteorological observations from offsite locations. WINData will deploy remote meteorological towers equipped with sensors and a PI data acquisition system to transmit high-fidelity wind speed and direction data. This upstream wind data will help forecasters better predict ramp events and refine short-term wind power forecasts to improve grid integration and reduce operating costs for utilities. WINData has partnered with OSIsoft to develop interfaces between their remote data acquisition hardware and the PI System and ECHO platforms.
Gantner presented at a workshop on optimized PV performance monitoring. They discussed trends in declining PV costs, the benefits of independent PV monitoring for risk reduction and increased asset value. Gantner's monitoring solutions provide real-time data processing, prediction models, and plant control functions to support grid operations. Advanced monitoring can improve O&M strategies and lower levelized cost of electricity.
Cloud-Computing and Energy Efficiency an holistic approach.Cisco Russia
This document discusses the benefits of cloud computing and energy efficiency in data centers. It notes that cloud computing allows for increased server utilization through virtualization and consumption-based pricing, which impacts providers' revenues. The cloud relies on 100% uptime, security, and efficient data centers to handle the growing amount of data and applications. Adopting cloud computing and improving data center efficiency can help reduce costs through better optimization and utilization of IT assets.
بررسی را ههاي بهبود امنیت مجازي سازي با استفاده از محاسبات قابل اعتماد yousef emami
مجازي سازي این امکان را به سرورهاي مجازي شده مجزا و امن می دهد تا روي یک سرور فیزیکی اجرا شوند و اجازه انتقال
ماشین هاي مجازي از یک سرور فیزیکی به سرور دیگر را به منظور صرفه جویی در انرژي و توازن باررا می دهد. فرایند انتقال
است به طور کلی یکی از بزرگترین ها نگرانی ها MAN-IN-MIDDLE و DOS ماشین هاي مجازي مستعد حملاتی مانند حملات
در این فرایند انتقال، امنیت است .محاسبات قایل اعتماد مجموعه اي از فناوري ها است که پشتیبانی سخت افزاري و نرم
افزاري براي ذخیره سازي امن وجامعیت نرم افزار فراهم می اورد.ترکیب محاسبات قابل اعتماد با سیستم هاي محاسباتی
مجازي شده ،امکان محافظت مبتنی بر سخت افزار از اطلاعات حساس و تشخیص نرم افزار مخرب که قصد تخریب عملیات
TPM(Trusted Platform محیط هاي مجازي شده را دارند را می دهد.این مقاله دیدگاههاي موجود در زمینه مجازي سازي
را بررسی می نماید. TPM و پروتکل هاي پیشنهاد شده جهت انتقال امن ماشین هاي مجازي با استفاده از Module)
This document summarizes a presentation on wind plant reliability given at a Sandia workshop. It discusses the need to take a holistic plant-level view of wind reliability rather than focusing on individual turbines. Current challenges include incomplete understanding of complex wind flows, limited data exploitation, and modeling capabilities lagging behind. Improved engineering practices, models validated with field data, and new technologies are needed, as well as leveraging internal R&D and external collaborations. Concerted field tests and simulations validated with measurement data are key to advancing modeling capabilities and overcoming barriers to new technologies.
The Friends of NELHA presented a 3 part workshop called Energy Efficiency and Auditing Workshop in Hawaii. This slideshow presentation by Dr. Roderick Hinman is the first section which discusses what electricity is, how it is measured, and how you can measure the electrical loads of each appliance in your home to make decisions that can save on your home electric bill.
Comparisons of building energy simulation softwaresZheng Yang
This document summarizes and compares several building energy simulation programs in terms of their capabilities to couple occupancy information with HVAC energy simulation. It finds that while many programs can perform energy simulation, few systematically analyze the relationship between occupancy and HVAC energy use. It identifies gaps in research and calls for studies that incorporate occupancy data into simulation and evaluate its effects on HVAC energy consumption and the response of HVAC systems to occupancy-based controls. The document also reviews commonly used simulation programs and finds that they use different approaches to model heat transfer, load calculation, occupancy-HVAC connection, HVAC modeling, and simulation, with varying degrees of accuracy, flexibility, and user friendliness.
Practical Experiences with Smart-Homes Modeling and SimulationSimulationX
Within the next years, more homes will be equipped with smart metering devices, intelligent devices and home energy management systems (HEMS). The EMS are designed to adapt Demand Side Management (DSM) to households. The goals behind the DSM can vary within the household. It can target shaving the load peaks, minimize CO2 emissions, or minimize the overall energy bill via controlling the in-house energy supply resources and intelligent consuming devices. Thus, the EMS represents the dominant ‘smart home’.
Through this contribution, different practices of smart home modeling will be presented in which SimulationX has been integrated under different configurations, software and hardware integrations. The developed models represent the state-of-the art of the current, upcoming and futuristic smart homes. The incentives behind developing these models will be deliberated, along with the economic advantages in its applications within the smart grid. Moreover, the experience behind using SimulationX for evaluating such models will be presented.
This document discusses various technologies for enabling demand response and reducing kW usage. It describes energy efficiency technologies, information and reporting systems, direct load control, distributed generation, energy management systems, smart load control systems, lighting technologies, and load shifting technologies that can be used to reduce demand. It also discusses considerations for determining the kW impacts and effects on facility operations when implementing demand reduction strategies.
This document presents a two-part study on temperature coefficients and thermal uniformity of PV modules and plants:
Part 1 examines the thermal uniformity of cells within modules and modules within PV plants through temperature mapping. It finds frame insulation reduces intra-module temperature differences the most. Backsheets with aluminum covers experience higher operating temperatures.
Part 2 determines climate-specific thermal model coefficients for PVsyst in Phoenix, Arizona based on a year of module performance data. The coefficients vary by technology and mounting, with polymer modules having higher coefficients than glass modules. For c-Si in Phoenix, the recommended coefficients are Uc=25.46 W/m2K and Uv=4.31 W/m3K
This document outlines a 4-step approach to reducing electricity costs through power monitoring. Step 1 is to monitor existing equipment usage to identify major power consumers. Step 2 determines cost-effective upgrade options using measured power costs. Step 3 seeks projects with the best return on investment, including utility incentives. Step 4 monitors post-project usage to validate savings and collect incentives. Real-world examples from manufacturing plants found potential savings of over $35,000 annually from optimizing air compressor usage. Monitoring a vacuum furnace also revealed $12,000 in annual savings from using older, less power-hungry equipment.
The document discusses NREL's wind energy systems engineering initiative, which aims to develop an integrated software framework for modeling wind plant performance and costs from a systems perspective. The framework integrates various turbine and plant models from NREL's WISDEM suite using OpenMDAO. Initial analyses demonstrate the impact of system-level coupling on sensitivity and optimization studies compared to previous simplified models. The framework allows flexible configuration and improvement of models to better capture interactions across the full wind energy system over its lifetime.
The document discusses improving data center energy efficiency (PUE) through implementing appropriate technologies. It explains key PUE concepts like shared infrastructure allocation and redundancy impacts. Realistic PUE expectations are 1.1-1.2 for ordinary data centers, while some report extreme low PUE through atypical strategies. Standardizing PUE calculations is important for valid comparisons between data centers.
Holistic District Heating Grid Design with SimulationX & Green CitySimulationX
Buildings are central elements of future smart grids. Heating and cooling demand are predictable within reason, building mass as well as heating and hot water systems provide inherent storage capacity. Additionally, the fluctuation between peak and average power of a building is much more friendly to the grid than of other network nodes like wind power or electric mobility.
A local heating grid partially supplied by renewable solar heat is currently being built in a town in Bavaria. Heat pump systems provide additional storage capacity for electric grid surplus while they serve as wind energy dump for the local utility company. Cogeneration plants and peak-power boilers provide heat and power in times of low energy coverage. The low temperature heating grid supplies decentral heat pumps, which provide required heat at a much higher temperature level to each building.
The paper describes basic modeling aspects for district heating grids with SimulationX & Green City. An interesting solar-aided grid example helps to identify benefits of a new modeling approach.
Wind power forecasting an application of machineJawad Khan
The advancement in renewable energy sector being the focus of research these days, a novel neuro evolutionary technique is proposed for modeling wind power forecasters.
The work uses the robust technique of
Cartesian Genetic Programming to evolve ANN
for development of forecasting models.
These Models predicts power generation of a wind based power plant from a single hour up to a year - taking a big lead over other proposed models by reducing its MAPE to minimum values for a single day hourly prediction.
Results when compared with other models in the literature demonstrated that the proposed models are among the best estimators of wind based power generation plants proposed to date.
This document summarizes recent and planned improvements to the System Advisor Model (SAM), a free software tool that estimates the cost and performance of renewable energy systems. SAM models photovoltaics, batteries, concentrating solar power, wind, geothermal, and biomass technologies. Recent updates include improved inverter and transformer loss models for PV, 3D shading capabilities, and expanded battery modeling options. Planned additions are a photovoltaic reliability model, mobile SDKs, and open sourcing the SAM code over the summer.
- NREL is a national laboratory operated by the U.S. Department of Energy that focuses on energy efficiency and renewable energy.
- The presentation introduces EDAPT and OpenStudio, which were created to lower the administrative costs and make energy analysis cheaper for Energy Design Assistance programs.
- EDAPT is a web service that tracks project status, manages data and communications, and reports program-wide outcomes. It integrates with OpenStudio and EnergyPlus to automate energy modeling and analysis.
The document discusses different electrical machines used for wind power generation. It begins with an introduction to wind power production and then describes various electrical generation schemes currently in use worldwide. These include constant speed wind turbines connected directly to the grid, variable speed turbines with a doubly-fed induction generator, and direct drive turbines with a synchronous or permanent magnet synchronous generator. The permanent magnet synchronous generator system directly connected to the grid via a full-scale power converter is highlighted as one of the most promising designs currently used due to its high efficiency and robustness.
More Reliable Wind Power Forecasting - OSIsoft Users ConferenceGregg Le Blanc
The document discusses WINData's plan to reduce wind forecasting uncertainty using real-time meteorological observations from offsite locations. WINData will deploy remote meteorological towers equipped with sensors and a PI data acquisition system to transmit high-fidelity wind speed and direction data. This upstream wind data will help forecasters better predict ramp events and refine short-term wind power forecasts to improve grid integration and reduce operating costs for utilities. WINData has partnered with OSIsoft to develop interfaces between their remote data acquisition hardware and the PI System and ECHO platforms.
Gantner presented at a workshop on optimized PV performance monitoring. They discussed trends in declining PV costs, the benefits of independent PV monitoring for risk reduction and increased asset value. Gantner's monitoring solutions provide real-time data processing, prediction models, and plant control functions to support grid operations. Advanced monitoring can improve O&M strategies and lower levelized cost of electricity.
Cloud-Computing and Energy Efficiency an holistic approach.Cisco Russia
This document discusses the benefits of cloud computing and energy efficiency in data centers. It notes that cloud computing allows for increased server utilization through virtualization and consumption-based pricing, which impacts providers' revenues. The cloud relies on 100% uptime, security, and efficient data centers to handle the growing amount of data and applications. Adopting cloud computing and improving data center efficiency can help reduce costs through better optimization and utilization of IT assets.
بررسی را ههاي بهبود امنیت مجازي سازي با استفاده از محاسبات قابل اعتماد yousef emami
مجازي سازي این امکان را به سرورهاي مجازي شده مجزا و امن می دهد تا روي یک سرور فیزیکی اجرا شوند و اجازه انتقال
ماشین هاي مجازي از یک سرور فیزیکی به سرور دیگر را به منظور صرفه جویی در انرژي و توازن باررا می دهد. فرایند انتقال
است به طور کلی یکی از بزرگترین ها نگرانی ها MAN-IN-MIDDLE و DOS ماشین هاي مجازي مستعد حملاتی مانند حملات
در این فرایند انتقال، امنیت است .محاسبات قایل اعتماد مجموعه اي از فناوري ها است که پشتیبانی سخت افزاري و نرم
افزاري براي ذخیره سازي امن وجامعیت نرم افزار فراهم می اورد.ترکیب محاسبات قابل اعتماد با سیستم هاي محاسباتی
مجازي شده ،امکان محافظت مبتنی بر سخت افزار از اطلاعات حساس و تشخیص نرم افزار مخرب که قصد تخریب عملیات
TPM(Trusted Platform محیط هاي مجازي شده را دارند را می دهد.این مقاله دیدگاههاي موجود در زمینه مجازي سازي
را بررسی می نماید. TPM و پروتکل هاي پیشنهاد شده جهت انتقال امن ماشین هاي مجازي با استفاده از Module)
An efficient ant optimized multipath routing in wireless sensor networkEditor Jacotech
Today, the Wireless Sensor Network is increasingly gaining popularity and importance. It is the more interesting and stimulating area of research. Now, the WSN is applied in object tracking and environmental monitoring applications. This paper presents the self-optimized model of multipath routing algorithm for WSN which considers definite parameters like delay, throughput level and loss and generates the outcomes that maximizes data throughput rate and minimizes delay and loss. This algorithm is based on ANT optimization technique that will bring out an optimal and organized route for WSN and is also to avoid congestion in WSN, the algorithm incorporate multipath capability..
The document discusses opportunities in health information technology resulting from HITECH and next steps for the national health IT infrastructure. It notes that critical mass, common platforms, and secondary value can drive network effects and IT adoption. Mature data environments that support value require stable policies, business alignment, standards, and value from data exchange. The document outlines various needs and opportunities around EMR adoption, meaningful use, health information exchange, interoperability standards, and policy environments to continue advancing health IT.
The document discusses enabling energy efficient data centers through the E3S Center. The E3S Center aims to develop electronic systems that are self-sensing and regulating, and optimized for energy efficiency at various performance levels. It works with industry and academia. Over a 5-year period, its goals include determining data center inefficiencies at all levels, developing predictive models and control algorithms for synergistic management of computing and cooling, and improving airflow and thermal management through testing and validation. The Center is led by researchers from Binghamton University, University of Texas at Arlington, and Villanova University, and partners with many industry collaborators.
A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTINGijujournal
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud computing requires many tasks to be executed by the provided resources to achieve good performance, shortest response time and high utilization of resources. To achieve these challenges there is a need to develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to optimize energy consumption. This study accomplished with all the existing techniques mainly focus on reducing energy consumption.
Energy efficient utilization of data center resources can be carried out by optimization of the resources allocated in virtual machine placement through live migration. This paper proposes a method to optimize virtual machine placement in Banker algorithm for energy efficient cloud computing to tackle the issue of load balancing for hotspot mitigation and proposed method is named as Optimized Virtual Machine Placement in Banker algorithm (OVMPBA). By determining the state of host overload through dynamic thresholds technique and minimization migration policy for VM selection from the overloaded host an attempt is made to efficiently utilize the available computing resources and thus minimize the energy consumption in the cloud environment. The above research work is experimentally simulated on CloudSim Simulator and the experimental result shows that proposed OVMPBA method provides better energy efficiency and lesser number of migrations against existing methods of host overload detection-virtual machine selection and therefore maximizes the cloud energy efficiency.
Energy efficient task scheduling algorithms for cloud data centerseSAT Journals
Abstract Cloud computing is a modern technology which contains a network of systems that form a cloud. Energy conservation is one of the major concern in cloud computing. Large amount of energy is wasted by the computers and other devices and the carbon dioxide gas is released into the atmosphere polluting the environment. Green computing is an emerging technology which focuses on preserving the environment by reducing various kinds of pollutions. Pollutions include excessive emission of greenhouse gas, disposal of e-waste and so on leading to greenhouse effect. So pollution needs to be reduced by lowering the energy usage. By doing this, utilization of resources should not be reduced. With less usage of energy, maximum resource utilization should be possible. For this purpose, many green task scheduling algorithms are used so that the energy consumption can be minimized in servers of cloud data centers. In this paper, ESF-ES algorithm is developed which focuses on minimizing energy consumption by minimizing the number of servers used. The comparison is made with hybrid algorithms and most-efficient-server first scheme. Keywords: Cloud computing, Green computing, Energy-efficiency, Green data centers and Task scheduling.
I presented "Cloudsim & Green Cloud" in First National Workshop of Cloud Computing at Amirkabir University on 31st October and 1st November, 2012.
Enjoy it!
Energy-efficient data centers: Exploiting knowledge about application and res...GreenLSI Team, LSI, UPM
Presentation by Jose M. Moya at the IEEE Region 8 SB & GOLD Congress (25 – 29 July, 2012).
The current techniques for data center energy optimization, based on
efficiency metrics like PUE, pPUE, ERE, DCcE, etc., do not take into
account the static and dynamic characteristics of the applications and
resources (computing and cooling). However, the knowledge about the
current state of the data center, the past history, the resource
characteristics, and the characteristics of the jobs to be executed
can be used very effectively to guide decision-making at all levels in
the datacenter in order to minimize energy needs. For example, the
allocation of jobs on the available machines, if done taking into
account the most appropriate architecture for each job from the
energetic point of view, and taking into account the type of jobs that
will come later, can reduce energy needs by 30%.
Moreover, to achieve significant reductions in energy consumption of
state-of-the-art data centers (low PUE) is becoming increasingly
important a comprehensive and multi-level approach, ie, acting on
different abstraction levels (scheduling and resource allocation,
application, operating system, compilers and virtual machines,
architecture, and technology), and at different scopes (chip, server,
rack, room, and multi-room).
Interference-Aware Multipath Routing In Wireless Sensor NetworksMinor projr...Rakesh Behera
Routing in wireless sensor networks has been considered an important field of research over the past decade. Wireless sensor network essentially consists of data Sensor Nodes and Video Sensor Nodes, which senses both sound and motion of events. Single path routing protocol has been used for route discovery. Though this protocol reduces computation complexity and resource utilization, there are some disadvantages like reduced network throughput, network performance, increased traffic load and delay in data delivery. To overcome these drawbacks a new protocol called Interference Aware Multi-path Routing(IAMR) is proposed to improve the reliability of data transmission, fault-tolerance, Quality of Service. Here, the traffic intersection spread out among the multiple paths. This technique is applied between the sources and sink to reduce routing overhead and energy consumption. The proposed protocol is simulated using NS2
This document is a thesis submitted by Moustafa Mohamad Najm to the Department of Computer Science and Engineering at the National Institute of Technology in partial fulfillment of the requirements for a Master of Technology degree. The thesis proposes an energy efficient load balancing algorithm for cloud computing using ant colony optimization to minimize energy consumption while providing required quality of service for customers in a heterogeneous environment. It considers the tradeoff between energy usage and performance by using current resource information and CPU capacity factors. The algorithm is implemented and evaluated using the CloudSim simulator.
This is my presentation, explaining the energy and carbon efficient algorithm presented in the conference paper published by the CLOUDS research lab, who developed the cloud simulator - CloudSim.
In mobile ad hoc networks (MANETs), multipath routing protocols are more popular due to overcomes the certain limitation of single path routing like lower end-to-end delay, load balancing, energy efficiency and network lifetime. By providing multiple paths between a source-destination pair, multipath routing protocols are avoid such above problems. AOMDV (Ad Hoc On-Demand Multipath Distance Vector) routing protocol is an on-demand multipath routing and which is a relatively maturity and extensive application protocol. It doesn’t consider residual energy and load situation of the node on the time of route discovery process. So AOMDV’s efficiency declines sharply in case of high load and fast moving velocity. To solve the above problems, we propose an improved protocol Energy Efficient- AOMDV (EAOMDV) of AOMDV routing algorithm. EAOMDV is based on a strategy of energy model and load balancing concept. It will consider the residual energy and the load situation of the nodes, when it starts the route discovery phase. After considering the above concept and according to the simulation results, the EAOMDV routing protocol improves the efficiency, the packet delivery ratio and reduces the routing load.
Energy consumption study of a WSN using 6TiSCH architectureFederico Sismondi
Motivated by the active developments on the industrial automated world and the new technologies arising in the
area of Internet of Things, the IETF, based on IEEE’s existing standards, and some already accepted protocols,
propose a new architecture to satisfy the needs of both fields.
6tisch, the new IETF’s architecture to be studied during our project, aims to give a convergent solution for both
fields that have plenty of common points. It aims to satisfy the requirements of the wireless low powered lossy networks. Among them, we can point out: energy management policy, energy efficient design, link reliability,
robustness, scalability support, interoperability, self organization, end to end reliability, security and mobility
support, as the most noticeable ones.
The project proposed aims to obtain a well founded experience on how the newly developed architecture 6tisch performs in the OpenWSN project. The partner enterprise wants to quantify the energy consumed by the motes in a real use case, with special detail on how the different parameterizations of the protocol stack would affect it.
Due to the increasing need of networks relying on low energy consumption, our project will analyze from the
lowest layers of the protocol stack how 6tish architecture performs energywise and how the different mechanisms like routing table construction, message forwarding function, scheduling of the TSCH slots, and many others will perform.
This document provides an overview of CloudSim, an open-source simulation toolkit for modeling and simulating cloud computing environments and applications. It discusses CloudSim's architecture, features, and applications. CloudSim provides a framework for modeling data centers, cloud resources, virtual machines, and cloud services to simulate cloud computing infrastructure and platforms. It has been used by researchers around the world for applications like evaluating resource allocation algorithms, energy-efficient management of data centers, and optimization of cloud computing environments and workflows.
This document provides an overview of several cloud simulation tools: CloudSim, CloudAnalyst, GreenCloud, and iCanCloud. CloudSim enables modeling and simulation of cloud computing infrastructures and applications. CloudAnalyst focuses on simulating large-scale cloud applications and studying their behavior under different deployment configurations using a graphical user interface. GreenCloud extends the NS2 network simulator to enable energy-aware cloud computing simulations at the packet level. iCanCloud allows modeling both existing and non-existing cloud architectures through a flexible hypervisor module and graphical interface to simulate distributed systems.
Overview on security and privacy issues in wireless sensor networks-2014Tarek Gaber
Lecture Outlines
Why Security is Important for WSN
WSNs have many applications e.g.:
military, homeland security
assessing disaster zones
Others.
This means that such sensor networks have mission-critical tasks.
Security is crucial for such WSNs deployed in these hostile environments.
Why Security is Important for WSN
Moreover, wireless communication employed by WSN facilitates
eavesdropping and
packet injection by an adversary.
These mentioned factors require security for WSN during the design stage to ensure operation safety, secrecy of sensitive data, and privacy for people in sensor environments.
Algorithms to achieve security services
Symmetric Encryption
Asymmetric Encryption
Hash Function/Algorithm
Digital Signature
Why Security is Complex in WSN
Because of WSNs Characteristics:
Anti-jamming and physical temper proofing are impossible
greater design complexity and energy consumption
Denial-of-service (DoS) attack is difficult
Sensor node constraints
Sensor nodes are susceptible to physical capture
Deploying in hostile environment.
eavesdropping and injecting malicious message are easy
Using wireless communication
Why Security is Complex in WSN
Because of WSNs Characteristics:
maximization of security level is challenging
Resource consumption
asymmetric cryptography is often too expensive
Node constraints
centralized security solutions are big issue
no central control and constraints, e.g. small memory capacity.
Cost Issues
Overall cost of WSN should be as low as possible.
Typical Attacks to WSN
Physical Attacks
Environmental
Permanently destroy the node, e.g., crashing or stealing a node.
Attacks at the Physical Layer
Jamming: transmission of a radio signal to interfere with WSN radio frequencies.
Constant jamming: No message are able to be sent or received.
Intermittent jamming: Nodes are able to exchange messages periodically
Jamming Attack Countermeasure
Physical Attacks
Node Capture Attacks
routing functionalities
Countermeasure
tamper-proof features
Expensive solution
Self-Protection
disable device when attack detected
Attacks on Routing
Sinkhole attack
attacker tries to attract the traffic from a particular region through it
Solution:
Watchdog Nodes can start to trace the source of false routing information
Attacks on Routing
Sybil attack (Identity Spoofing)
attacker claims to have multiple identities or locations
provide wrong information for routing to launch false routing attacks
Solutions:
Misbehavior Detection.
Identity Protection
Privacy Attacks
Attempts to obtain sensitive information collected and communicated in WSNs
Eavesdropping
made easy by broadcast nature of wireless networks
Traffic analysis
used to identify sensor nodes of interest (data of interest),
WSN Privacy Issues Cont.
WSN Privacy Issues Attack
Trust and reputation in WSN
WSN Traditional Security Techniques
Cryptographic primitive
This document provides an overview and tutorial on using CloudSim, an open-source simulation toolkit for modeling and simulation of cloud computing infrastructures and applications. It discusses CloudSim's features and architecture, prerequisites for using it, and how to set up the development environment in Eclipse. Sample code examples are presented to demonstrate running simulations of data centers with hosts and cloudlets using CloudSim.
The document discusses green IT and increasing energy efficiency. It covers topics like improving power distribution systems, using more efficient generators and power supplies, adopting energy management software, and generating power from renewable sources. It also discusses challenges facing data centers like increasing power and cooling demands, and solutions to improve availability like deploying redundant power systems and parallel power supplies.
Electrical Audit of Computer Labs on CampusMichael Pérez
The document summarizes an electrical audit of computers on campus to analyze their energy consumption and carbon emissions. It outlines developing an Excel calculator to input equipment in campus computer labs and output their estimated energy usage. A pilot study was conducted on two labs which found significant potential savings in energy costs and carbon footprint from more efficient equipment usage. The recommendation is to gather full inventory data from all campus labs to identify further opportunities to reduce energy consumption and promote sustainability initiatives.
Green & Beyond: Data Center Actions to Increase Business Responsiveness and R...IBMAsean
The document discusses actions that data centers can take to increase responsiveness, reduce costs, and become more environmentally friendly. It outlines five building blocks: diagnose energy usage, build energy efficient infrastructure, optimize cooling, implement virtualization, and continuously measure and manage energy usage. Data centers that follow these principles can achieve 40-50% energy savings, reduce operational costs by $1.3 million per year, and lower their environmental impact by reducing emissions equivalent to 1,300 cars.
E source energy managers conf 4 24-13-finaljosh whitney
This document discusses best practices for improving efficiency in small server rooms and closets. It begins with an introduction on metrics to measure efficiency like PUE, CUE, and RUE. Unique challenges for small spaces are split incentives and lack of scale. Best practices discussed include improving infrastructure efficiency through techniques like hot/cold aisle containment and raising temperature setpoints. Improving IT efficiency through server refresh, consolidation, virtualization and powering off unused servers is also covered. Case studies show significant potential savings through these approaches.
Sklubi AlumniWeekend 23.10.2010:
Reijo Maihaniemi
Electricity Consumption: General
Electricity Savings Through DC Power Feed
DC Data Center Projects in the World
ICT Energy saving actions
This document discusses three key factors to consider when designing a data center's power distribution infrastructure: 1) the size of the system based on the data center's power needs, 2) the reliability architecture (tier level) which impacts redundancy requirements, and 3) the operational complexity depending on the reliability architecture. Understanding these three factors provides a foundation for designing an integrated electrical power distribution system suited to the data center's needs.
This document discusses the utility and limitations of PUE (Power Usage Effectiveness) as a data center efficiency metric. While PUE is a useful high-level metric, it does not provide enough detail to optimize efficiency. PUE only measures the ratio of total facility power to IT equipment power, but does not account for factors like server utilization, resilience, or diversity of the IT load. The document argues that more detailed energy monitoring data is needed at the server, rack, and application level over time to properly evaluate efficiency and enable tangible efficiency actions.
Neural network modeling and control of data centers is presented. Data centers consume significant and increasing amounts of energy. A neural network model is developed and trained using steady state and transient data from a physical data center setup to map temperature outputs. The neural network accurately models temperatures with 95% accuracy. A neural network controller is then designed using the inverse model to stabilize temperatures according to reference values in response to varying workloads and power consumption. The controller successfully regulates temperatures in real-time simulation. Future work includes implementing the control on an actual system and expanding the control parameters.
The document discusses data center tiers, components, design considerations, and costs. Tier classifications range from basic to fault tolerant, with higher tiers offering greater reliability but requiring more investment. Initial costs to build a 30,000 square foot Tier 3 facility range from $12-36 million on average $22 million. Annual operating costs range from $1-4 million on average $3.5 million. The document also provides an overview of key data center infrastructure components like cooling, power, racks and cabling.
1) Calculating the total power requirements for a data center involves estimating the power needs for critical IT loads, future loads, UPS systems, lighting, and cooling equipment.
2) The process begins by determining the critical IT load based on equipment specifications and then accounting for future growth. UPS loads are estimated based on efficiency and battery charging needs.
3) Lighting and cooling loads are calculated based on data center size. The total power estimate is then used to size the electrical service and generator capacity while accounting for peak loads and safety factors.
Dr. Tamar Eilam discusses sustainable computing and AI sustainability. Deep learning requires a lot of computation and energy to train large models. The demand for AI is growing exponentially, as are the sizes of language models. Foundation models are becoming more common, where a broad pre-trained model is adapted for specific tasks. However, continuously training larger models risks increasing energy consumption significantly. Sustainable AI research aims to dynamically track energy and carbon usage, while helping data scientists determine optimal model training strategies based on transparency around computational costs and model performance.
IoT-Based Secure Energy Pricing Management Controller.pptxAliSalman110
This document summarizes a thesis on implementing an optimized smart home energy management system using IoT applications and the PSO optimization algorithm. It describes a smart plug that monitors and controls appliances remotely, a Raspberry Pi-based energy management controller (EMC) that schedules appliances using MQTT, and a mobile app for remote monitoring and control. Experimental results found that using PSO to schedule appliances based on time-of-use pricing achieved a 24.31% reduction in energy costs compared to other methods. The conclusions discuss using smart plugs and the EMC to accurately read appliance consumption data and schedule appliances optimally via MQTT to reduce user costs.
This slide is an introductory part of the course Computer Application in Power system. it will describe the basic tasks of a computer and different computer application areas.
Green data Data Center is the only way going forward.To improve efficiency and PUE count we are leveraging technology and resources to cut down on emissions and utilizing power in a better way to reduce losses.This project speaks about the latest trends in Green IT and also how can banks use the technologies to upgrade their legacy systems
An exploration of the benefits and limitations of the popular Power Usage Effectiveness (PUE) metric, for gauging datacenter efficiency.
How to avoid the pitfalls inherent in the definition of PUE; and some suggested means by which the PUE concept can be enhanced in real-world applications.
CORPORATE PLUG: For more information about how Raritan helps solve this problem, I encourage you to see: http://www.raritan.com/resources/screenshots/power-iq/
ARC's Larry O'Brien Process Automation Presentation @ ARC Industry Forum 2010ARC Advisory Group
ARC's Larry O'Brien Process Automation Presentation @ ARC Industry Forum 2010 in Orlando, FL.
Using Process Automation to Optimize Energy Consumption
The Cost of Energy
How Well is Energy Managed in Today’s Plants?
Using Your Process Automation Infrastructure
with an Eye Toward Optimizing Energy
Consumption
The Business Value of Integrated Power &
Automation
Enabling Technologies
Training Your People and Managing Knowledge
Moving Forward
This document discusses energy efficiency strategies for data centers. It begins by providing an overview of metrics used to measure data center efficiency like power usage effectiveness (PUE) and data center infrastructure efficiency (DCIE). It then discusses cooling strategies, noting that cooling can consume around 45% of a data center's total energy. Specific strategies mentioned include air-side free cooling, water-side free cooling, and the Facebook data center in Prineville, Oregon which uses free cooling. The document also discusses the benefits of using DC power distribution instead of AC to reduce energy losses during conversion. It provides an overview of virtualization and the evolution of data center architecture.
Honeypots Unveiled: Proactive Defense Tactics for Cyber Security, Phoenix Sum...APNIC
Adli Wahid, Senior Internet Security Specialist at APNIC, delivered a presentation titled 'Honeypots Unveiled: Proactive Defense Tactics for Cyber Security' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
Decentralized Justice in Gaming and EsportsFederico Ast
Discover how Kleros is transforming the landscape of dispute resolution in the gaming and eSports industry through the power of decentralized justice.
This presentation, delivered by Federico Ast, CEO of Kleros, explores the innovative application of blockchain technology, crowdsourcing, and incentivized mechanisms to create fair and efficient arbitration processes.
Key Highlights:
- Introduction to Decentralized Justice: Learn about the foundational principles of Kleros and how it combines blockchain with crowdsourcing to develop a novel justice system.
- Challenges in Traditional Arbitration: Understand the limitations of conventional arbitration methods, such as high costs and long resolution times, particularly for small claims in the gaming sector.
- How Kleros Works: A step-by-step guide on the functioning of Kleros, from the initiation of a smart contract to the final decision by a jury of peers.
- Case Studies in eSports: Explore real-world scenarios where Kleros has been applied to resolve disputes in eSports, including issues like cheating, governance, player behavior, and contractual disagreements.
- Practical Implementation: Detailed walkthroughs of how disputes are handled in eSports tournaments, emphasizing speed, cost-efficiency, and fairness.
- Enhanced Transparency: The role of blockchain in providing an immutable and transparent record of proceedings, ensuring trust in the resolution process.
- Future Prospects: The potential expansion of decentralized justice mechanisms across various sectors within the gaming industry.
For more information, visit kleros.io or follow Federico Ast and Kleros on social media:
• Twitter: @federicoast
• Twitter: @kleros_io
Securing BGP: Operational Strategies and Best Practices for Network Defenders...APNIC
Md. Zobair Khan,
Network Analyst and Technical Trainer at APNIC, presented 'Securing BGP: Operational Strategies and Best Practices for Network Defenders' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
1. Energy Efficient Data Centers
Update on LBNL data center
energy efficiency projects
June 23, 2005
Bill Tschudi
Lawrence Berkeley National
Laboratory
Wftschudi@lbl.gov
2. LBNL’s energy research
related to data centers
Energy research roadmap
Case studies and energy benchmarking
Best practice identification
Self benchmarking protocol
Investigate efficiency of power supplies in IT
equipment
Investigate efficiency of UPS systems
Metrics for computing performance vs. energy
Technology transfer
Demonstration projects
3. Data center efficiency opportunity
Many efficiency ideas have been
identified through industry feedback
Case studies are helping to identify
best practices
4. Data center efficiency resources
ASHRAE “Thermal Guidelines for Data Processing
Environments”
ASHRAE “Power Trends and Cooling Applications”
In preparation: ASHRAE “Design Considerations for
Data Center and Communications Equipment Centers”
which includes a chapter on energy efficiency
5. Case studies/benchmarks
California
• Storage device and router
Mfgs.
• Banks
• Web hosting facilities
• Internet service provider
• State tax center
• Federal facilities
New York
• Recovery center (hosting)
• Financial institution
6. IT equipment load intensities
Data collected in 1999 through 2003 showed
that electrical power intensity for IT equipment
alone was on the order of 25 Watts/sf.
Current data suggests that load intensities are
rising through compaction and/or due to rising
equipment power consumption.
7. 2003 IT equipment loads
from LBNL case studies
Computer Load Density
70
60
50
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Facility
W/sq.ft.
Average 27 +/-
(W/sf of electrically active floor space using Uptime definition)
8. 2003 projections if fully loaded
Current and Projected Load Intensity
100
80
60
40
20
0
Projected
Average 44
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Facility
W/sq. ft.
Current Computer Load Projected Computer Load
(W/sf of electrically active floor space using Uptime definition)
9. Distribution of computer load intensities
reported to Uptime Institute
1.00
0.80
0.60
0.40
0.20
0.00
Source: Uptime Institute, 2002.
0 20 40 60 80 100
Computer room UPS power (Watts/square foot)
Fraction of total floor area in sample
1999
2000
2001
Number of
facilities Total floor area
Computer room
power density
Million square feet W/square foot
1999 35 1.55 22.9
2000 38 1.72 22.4
2001 48 1.86 25.3
10. 2005 IT equipment benchmarks
IT equipment load
100
90
80
70
60
50
40
30
20
10
0
1 2 3 4 5 6 7
Data center identifier
Watts/sf
LBNL NERSC
supercomputer
Average 52
w/sf
11. Electrical power conversion
is a big opportunity
Every conversion of AC voltage, AC to DC, DC to AC,
or DC voltage conversion results in loss of electrical
power and corresponding heat that must be removed
from the data center. Minimizing this conversion loss
has a magnifying effect that allows all facility systems to
use less energy and consequently the infrastructure
systems can be downsized.
Saving say 10% on the conversion loss could result in
20% or more saving for the facility.
12. How many times do data centers convert
In Out
Inverter
Bypass
Battery/Charger
Rectifier
Internal Drive
External Drive
I/O
Memory Controller
m Processor
SDRAM
Graphics Controller
5V
12V
3.3V
12V 1.5/2.5
AC/DC DC/DC
DC/DC
AC/DC Multi output PS
V
1.1V-
1.85V
12V
3.3V
3.3V
Voltage Regulator Modules
PWM/PFC
Switcher
Unregulated DC
To Multi Output
Regulated DC
Voltages
AC and DC?
AC voltage conversions
16. 131
Electricity use in a server
32 32
72
41
86
27 32
140
120
100
80
60
40
20
0
AC DC
Losses
DC/DC
Losses
Fans
Drives
PCI Cards
Processors
Memory
Chipset
Based on a typical dual processor 450W 2U Server;
Approximately 160W out of 450W (35%) are losses in the
power conversion process (Source: Brian Griffith: INTEL)
17. Power supply opportunity
2380
85
170
1190
4335 340
HVAC Chilled Water
Standby Generator
Lighting and Plug
Loads
UPS Losses
Computing Load
HVAC Fan Load
Efficiency
of Power
Conversion
Process
IT Load
(kW)
UPS
Losses
(kW)
Total
Savings
(kW)
65% 4335 340 0
70% 4025 316 334
75% 3757 295 623
80% 3522 276 877
85% 3315 260 1100
90% 3131 246 1299
Based on one case study approximately 4335 KW of a total of 8500 kW was IT
load. Assuming a 65% existing baseline efficiency, the savings opportunity using
90% efficient conversion process is approximately 1300kW not including any
savings from HVAC
18. Power supply efficiency recommendations
The Server Systems Infrastructure group (SSI)
publishes recommended minimum efficiencies
for server power supplies. The LBNL project
team is working with this group to see what can
be done to raise the bar.
19. Power supply efficiency today
68
Full Load
Efficiency >
68%
Redundant System of Power Supplies for Servers
21. Energy efficiency opportunity
Specifiers of UPS’s or IT equipment can have a huge
impact on energy use by requiring higher efficiencies.
Testing data shows that higher efficiencies can be
obtained – you have to ask for it. Facility and IT
professionals by working together can optimize overall
power conversions. Additional costs (if any) for more
efficient conversions will have a very short payback or
may be entirely justified by reductions in infrastructure.
22. HVAC (as a % of total load)
60%
50%
40%
30%
20%
10%
0%
1 2 3 4 5 6 7 8 9 10 11 12
Data Center Identifier
% of total load
Effectiveness of HVAC systems
23. Index of performance
The Uptime Institute proposed a metric to
evaluate the total efficiency of infrastructure
systems:
Index of performance = building systems KW ¸
UPS output
(i.e. ratio of building systems to IT equipment
load)
24. Look at the end-use
Data Center A Data Center B
Computer
Loads
38%
UPS Losses
6%
Lighting
2%
HVAC
54%
Total Power = 580
kW
UPS Losses
13%
HVAC - Air
Movement
9%
Computer
Loads
63%
HVAC -
Chilled Water
Plant
14%
Lighting
1%
Total Power = 1700 kW