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
Free Cooling: A Complete Solution on Reducing Total Energy Consumption for Te...Ehsan B. Haghighi
Accordingly the number of telecommunication base stations is increasing all over the world. Consequently network operators are looking for smart energy management architecture for their base stations. This presentation addresses more energy efficient cooling strategies in order to reduce this figure. Combining air conditioning and free cooling systems (e.g. extracting fresh air into the envelope for the latter) is one of the promising and well-proven methods to reduce energy consumption in base stations. Furthermore, the potential of employing free cooling in either single zone (IT/electronic equipment and batteries in one envelope), or dual zones (IT/electronic equipment and batteries in two separate envelopes) strategies are investigated.
VTT:n mukaan tuuliennusteiden virheistä aiheutuvat kustannukset voidaan jopa puolittaa, kun tuulivoimaennusteet tehdään useille maantieteellisesti hajautetuille tuulipuistoille yksittäisten tuulipuistojen sijasta. VTT on myös selvittänyt tuotantovaihtelujen tasaantumista Pohjoismaiden alueella. Tuloksia voidaan hyödyntää säätövoimatarpeen suunnittelussa ja tuulivoiman vaihtelun ennustamisessa.
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
Free Cooling: A Complete Solution on Reducing Total Energy Consumption for Te...Ehsan B. Haghighi
Accordingly the number of telecommunication base stations is increasing all over the world. Consequently network operators are looking for smart energy management architecture for their base stations. This presentation addresses more energy efficient cooling strategies in order to reduce this figure. Combining air conditioning and free cooling systems (e.g. extracting fresh air into the envelope for the latter) is one of the promising and well-proven methods to reduce energy consumption in base stations. Furthermore, the potential of employing free cooling in either single zone (IT/electronic equipment and batteries in one envelope), or dual zones (IT/electronic equipment and batteries in two separate envelopes) strategies are investigated.
VTT:n mukaan tuuliennusteiden virheistä aiheutuvat kustannukset voidaan jopa puolittaa, kun tuulivoimaennusteet tehdään useille maantieteellisesti hajautetuille tuulipuistoille yksittäisten tuulipuistojen sijasta. VTT on myös selvittänyt tuotantovaihtelujen tasaantumista Pohjoismaiden alueella. Tuloksia voidaan hyödyntää säätövoimatarpeen suunnittelussa ja tuulivoiman vaihtelun ennustamisessa.
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.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Saving energy in data centers through workload consolidationEco4Cloud
This whitepaper on recently co‑authored with 4 top-notch European excellence centers such as the Institute for High Performance Computing and Networking of the Italian National Research Council, the Department of Electronics and Telecommunications at Politecnico di Torino, eERG – Energy Department at Politecnico di Milano and PrimeEnergyIT/EfficientDataCenters frames the whole workload consolidation topic and provides an overview of state-of-the-art approaches, including E4C’s of course.
Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, building automation, automated highway, air transportation, and smart grid. Transmitting sensor measurements and control commands over wireless links provide many benefits such as the ease of installation and maintenance, low complexity and cost, and large flexibility to accommodate the modification and upgrade of the components in many control applications. Several industrial organizations, such as International Society of Automation (ISA), Highway Addressable Remote Transducer (HART), and Wireless In- dustrial Networking Alliance (WINA), have been actively pushing the application of wireless technologies in the control applications. Building a WNCS is very challenging since control systems often have stringent requirements on timing and reliability, which are difficult to attain by wireless sensor networks due to the adverse properties of the wireless communication and limited battery resources of the nodes. We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems.
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.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Saving energy in data centers through workload consolidationEco4Cloud
This whitepaper on recently co‑authored with 4 top-notch European excellence centers such as the Institute for High Performance Computing and Networking of the Italian National Research Council, the Department of Electronics and Telecommunications at Politecnico di Torino, eERG – Energy Department at Politecnico di Milano and PrimeEnergyIT/EfficientDataCenters frames the whole workload consolidation topic and provides an overview of state-of-the-art approaches, including E4C’s of course.
Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, building automation, automated highway, air transportation, and smart grid. Transmitting sensor measurements and control commands over wireless links provide many benefits such as the ease of installation and maintenance, low complexity and cost, and large flexibility to accommodate the modification and upgrade of the components in many control applications. Several industrial organizations, such as International Society of Automation (ISA), Highway Addressable Remote Transducer (HART), and Wireless In- dustrial Networking Alliance (WINA), have been actively pushing the application of wireless technologies in the control applications. Building a WNCS is very challenging since control systems often have stringent requirements on timing and reliability, which are difficult to attain by wireless sensor networks due to the adverse properties of the wireless communication and limited battery resources of the nodes. We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems.
Arduino and sensors for water level, soil moisture, temperature & relative humidity for application in the ClimaAdapt Project areas - Nagarjuna Sagar Project Left and Right Canals in the States of Telangana and Andhra Pradesh for water use efficiency - Canal and On Farm
Arduino based intelligent greenhouse ProjectAmit Saini
Final Year Project : - Arduino based ‘Intelligent Green House'
A complete greenhouse monitoring and controlling system ,that is automated, updating each and every detail on internet that can be accessed from anywhere. For sensors, it uses a light sensor, temperature sensor, moisture sensor, humidity sensor and all the updates will be available on internet through Ethernet shield through which the user can take care of the garden even when not at home.
Green computing is the practice of designing, developing, using, and disposing of computer hardware, software, and systems in an environmentally friendly way. This involves reducing the environmental impact of computing by minimizing energy consumption, reducing electronic waste, and using sustainable materials. Green computing is becoming increasingly important as the use of technology continues to grow and the environmental impact of technology becomes more apparent.
⭐⭐⭐⭐⭐ Learning-based Energy Consumption PredictionVictor Asanza
✅ Published in: https://doi.org/10.1016/j.procs.2022.07.035
As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL,
which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.
⭐ The matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter
⭐ The dataset used for data processing are available in:https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset
✅ Read more related topics:
https://vasanza.blogspot.com/
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
GreenDisc: A HW/SW energy optimization framework in globally distributed comp...GreenLSI Team, LSI, UPM
Marina Zapater attends as speaker to UCAmI 2012.
The main goal of this conference is to provide a discussion forum where researchers and practitioners on Ubiquitous Computing and Ambient Intelligence can meet, disseminate and exchange ideas and problems, identify some of the key issues related to these topics, and explore together possible solutions and future works.
The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991, has recently evolved to a more general paradigm known as Ambient Intelligence (AmI). Ambient Intelligence then represents a new generation of user-centred computing environments aiming to find new ways to obtain a better integration of the information technology in everyday life devices and activities.
Marina has presented our first results within the GreenDISC project, proposing several research lines that target the power optimization in computing systems. In particular, we deal with two novel and highly differentiated computer paradigms that, however, coexist and interact in the current application scenarios: the Wireless Sensor Networks (WSN) and the high-performance computing in Data Centers (DC).
For further information, please, refer to the paper:
M. Zapater, J. L. Ayala, and J. M. Moya, “GreenDisc: a HW/SW energy optimization framework in globally distributed computation,” , J. Bravo, D. López-de Ipiña, and F. Moya, Ed., Springer Berlin Heidelberg, 2012, pp. 1-8. doi:10.1007/978-3-642-35377-2_1
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Optimización energética de centros de datos aprovechando el conocimiento de l...GreenLSI Team, LSI, UPM
Talk “Advances in Electronic Systems Engineering” seminar, within the M.Sc. in Electronic Systems Engineering (MISE), to present the session on Energy Optimization in Data Centers.
Speech title: Energy efficiency beyond PUE: exploiting knowledge about application and resources
Abstract: 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).
Date and Time: Tuesday, October 15, 2013, 16:00, room B-221
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).
Eficiencia Energética Más Allá Del PUE: Explotando el Conocimiento de la Apli...GreenLSI Team, LSI, UPM
Conferencia invitada de Jose M. Moya en Datacenter Dynamics Converged Madrid 2012.
Las técnicas actuales de optimización energética de datacenters, basadas en métricas de eficiencia como PUE, pPUE, ERE, DCcE, etc., no tienen en cuenta las características estáticas y dinámicas de las
aplicaciones y los recursos (de computación y refrigeración). Sin embargo, el conocimiento del estado actual del datacenter, de la historia pasada, de las caracteriìsticas térmicas de los recursos y de las caracteriìsticas de demanda energética de los trabajos a ejecutar puede ser utilizado de manera muy eficaz para guiar la toma de decisiones a todos los niveles en el datacenter con objeto de minimizar las necesidades energeìticas. Por ejemplo, el reparto de trabajos en las maìquinas disponibles, si se hace teniendo en cuenta las arquitecturas maìs adecuadas para cada trabajo desde el punto de vista energeìtico, y teniendo en cuenta el tipo de trabajos que van a venir con posterioridad, puede reducir las necesidades energeìticas hasta un 30%.
Además, para conseguir una reducción significativa del consumo energético de datacenters ya eficientes (PUE bajo) cada vez es más importante un enfoque global y multi-nivel, esto es, actuando sobre los diferentes niveles de abstraccioìn del datacenter (planificación y asignación de recursos, aplicación, sistema operativo, compiladores y máquinas virtuales, arquitectura y tecnología), y en los distintos ámbitos (chip, servidor, rack, sala y multi-sala).
Proactive and reactive thermal optimization techniques to improve energy effi...GreenLSI Team, LSI, UPM
Marina Zapater presents her work at the PICATA Workshop. This workshop is intended to know the diverse groups of people recently incorporated thank to PICATA programme of Moncloa campus and who are researching and assessing the clusters.
The Program for International Talent Recruitment (PICATA) has focused on bringing in students and researchers from all over the world, in a determined effort towards internationalization and talent recruitment with different actions. The PICATA Programme offers sholarships for the development of PhD thesis marked by at least two practising doctors from the two associated Universities, the UCM and the UPM, with the possibility of participation by doctors from the other associated Institutions within the context of the Campus Moncloa in these areas: Global Change and New Energies, Materials for the Future, Agri-food and Health, Innovative Medicine, and Heritage.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
A Cyber Physical Approach to a Combined Hardware-Software
1. A Cyber Physical Approach to
Combined HW-SW Monitoring for
Improving Energy Efficiency in
Data Centers
Josué Pagán, Marina Zapater, Oscar Cubo, Patricia
Arroba, Vicente Martín and José M. Moya
Universidad Politécnica de Madrid
1 / 20
2. Contents
1. Power consumption problem in Data Centers
I. Introduction
II. Related work
2. Optimization Framework & Data analysis
I. A Cyber-physical system
II. Data analysis and sensor configuration
3. Results
4. Conclusions
2
4. 1. Power consumption
problems in Data Centers
• The numbers of the energy problem:
– DC world power consumption >1.3%
– In urban areas >50% of DC exceeds power grid capacity
– USA: 80 TWh/year in 2011 = 1.5 x NY
Power >600 TWhr expected in
2015 in the global footprint
•
Data Centers’ power consumption
is unsustainable
Projection of total electricity use by datacenters in the US and the world based on Koomey’s and EPA’s data
4
5. 1. Power consumption
problems in Data Centers
• Related work (approaches)
Cooling
• Allow the room temperature to increase
• Longer task → cooler server
• Balancing workload between servers
Computation • Reducing voltage/ frequency (DVFS)
– These two approaches are not enough individually
Holistic
(IT+cooling)
• Room environment affects (environmental monitoring)
• Measuring server, workload and environmental variables
to improve energy efficiency → usage of a CPS
5
6. 1. Consumption problems in
Data Centers
Requirements
Energy optimization
Our contributions
Make a holistic optimization
framework including environmental,
server and workload information
Dynamically adapt on runtime to
workload and environment
Gather, monitor and analyze in real
time
Gather useful data at the appropriate
rate
In a non-intrusive way, reducing the
data collected with an adaptable
sampling rate
6
8. 2. Optimization Framework &
Data analysis
• One step ahead. Optimization
– 80% Wpeak – 30% of workload (↓η)
GATHER
DATA
PROPOSE
OPTIMIZATIONS
GENERATE
KNOWLEDGE
– An energy model supposes apply optimizations over the Data Center
8
9. 2. Optimization Framework &
Data analysis
• Monitoring
– How a Data Center works?
– 30-50% cooling→ energy optimization
9
10. 2. Optimization Framework &
Data analysis
• What measure and why
– Environmental monitoring
Inlet and outlet temperature
Differential pressure
– Server monitoring
Server consumption, CPU temperature, fan speed
• …to predict
10
11. 2. Optimization Framework &
Data analysis
• How…
• exploring sampling intervals
– Temperature and power values
for AMD server under the
benchmark SPEC CPU 2006
– Different sampling rates for
different parameters
11
12. 2. Optimization Framework &
Data analysis
• Using… Multilevel star topology architecture
WSN
- Reconfigurable low -power:
only useful data without
information loss
- Adapt to changes in the
environment
RM
- Spatio-temporal allocation
- Possibly to change
decisions if needed
Gateway
-Fan-less, managed with a light OS
-Receive, store, analyze and convert
data. Establishes a timestamp.
-Sends data to the opt. platform
Server Sensors
- Internal sensors
- Polled via SW
Air conditioning
- Exhaust
temperature,
RH% and airflow
12
14. 3. Results
WSN deployment
•
Applied over Magerit Supercomputer in CeSViMa Supercomputing and Visualization Center
of Madrid
•
Cluster 9 racks 260 servers
14
15. 3. Results
– The goal: develop techniques to allow energy optimization in real environments
– With reconfigurable sampling rate:
– we achieve up to 68 % of reduction in gathered data
– Increase the WSN’ s life time depending on the occupancy
15
17. 4. Conclusions
Energy efficiency has to be faced in a holistic way
We propose an optimization framework monitoring environmental,
server and workload parameters
After a first monitoring study: a WSN has been deployed to gather
environmental data
Up to a 68% of reduction in the amount of gathered data
Maximizing the life time of WSN nodes
Solution applied in a real case study
17
18. Thank you for your attention
FIN
This project has been funded in part by the INNPACTO LPCLOUD: "Optimal Management Of low-power modes in cloud computing" IPT2012-1041-430000, developed in collaboration with Elite Ermestel and Converging Technologies and the CDTI project CALEO:
Distribution of operational thermal and optimization of energy consumption in data centers, "developed in collaboration with INCOTEC.
The author gratefully acknowledges the computer resources, technical expertise and assistance provided by the Supercomputing and
18
Visualization Center of Madrid (CeSViMa).
19. 4. Results and Conclusions
• Results: gathering data
• Inlet and outlet temperature
19
20. Magerit Supercomputer
• Cluster 9 racks 260 servers
245 are IBM PS702 2S
o 16 Power7 processors @ 3.3 GHz
o 32 GB of RAM
15 are IBM HS22
o 8 Intel Xeon processors @ 2.5 GHz
o 96 GB of RAM
200 TB of storage
20