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).
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.
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.
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.
Enerji Sektöründe Endüstriyel IoT Uygulamaları - Şahin Çağlayan (Reengen)ideaport
Reengen Enerji IoT Platformu kurucu ortağı ve AR-GE sorumlusu Sahin Çaglayan, nesnelerin interneti ve büyük veri analizi yeteneklerini bir araya getirerek ticari binalarda ve enerji şebekesinde bulut tabanlı optimizasyon süreçlerini anlattı.
-
23 Mart 2016
meet@ideaport | IoTxTR#21 'Enerji Sektöründe Endüstriyel IoT Uygulamaları' Semineri
Transforming Your Business Through Cloud ComputingAMD
AMD Senior Vice President and Chief Information Officer, Mike Wolfe, for offers insight into how AMD is using leading virtualization and cloud computing technologies to transform its business - reducing costs, increasing efficiency and bolstering productivity.
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.
Enerji Sektöründe Endüstriyel IoT Uygulamaları - Şahin Çağlayan (Reengen)ideaport
Reengen Enerji IoT Platformu kurucu ortağı ve AR-GE sorumlusu Sahin Çaglayan, nesnelerin interneti ve büyük veri analizi yeteneklerini bir araya getirerek ticari binalarda ve enerji şebekesinde bulut tabanlı optimizasyon süreçlerini anlattı.
-
23 Mart 2016
meet@ideaport | IoTxTR#21 'Enerji Sektöründe Endüstriyel IoT Uygulamaları' Semineri
Transforming Your Business Through Cloud ComputingAMD
AMD Senior Vice President and Chief Information Officer, Mike Wolfe, for offers insight into how AMD is using leading virtualization and cloud computing technologies to transform its business - reducing costs, increasing efficiency and bolstering productivity.
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
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).
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
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Energy-efficient data centers: Exploiting knowledge about application and resources
1. CAMPUS OF
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
Energy-efficient data centers:
Exploiting knowledge about
application and resources
José M. Moya <jm.moya@upm.es>
Integrated Systems Laboratory
José M.Moya | Madrid (Spain), July 27, 2012 1
2. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Data centers
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 2
3. CAMPUS OF
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 3
4. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Power distribution
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 4
5. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Power distribution (Tier 4)
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 5
6. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Contents
“Ingeniamos el futuro”
• Motivation
• Our approach
– Scheduling and resource
management
– Virtual machine
optimizations
– Centralized management
of low-power modes
– Processor design
• Conclusions
José M.Moya | Madrid (Spain), July 27, 2012 6
7. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Motivation
“Ingeniamos el futuro”
• Energy consumption of data centers
– 1.3% of worldwide energy production in 2010
– USA: 80 mill MWh/year in 2011 = 1,5 x NYC
– 1 data center = 25 000 houses
• More than 43 Million Tons of CO2 emissions per
year (2% worldwide)
• More water consumption than many industries
(paper, automotive, petrol, wood, or plastic)
Jonathan Koomey. 2011. Growth in Data center electricity use 2005 to 2010
José M.Moya | Madrid (Spain), July 27, 2012 7
8. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Motivation
“Ingeniamos el futuro” 35000
World server installed base
30000
• It is expected for total data 25000
(thousands)
20000 High-end servers
center electricity use to 15000 Mid-range servers
10000
exceed 400 GWh/year by 5000
Volume servers
2015. 0
2000 2005 2010
• The required energy for 5,75 Million new servers per year
cooling will continue to be at 10% unused servers (CO2 emissions
least as important as the similar to 6,5 million cars)
energy required for the
300
computation. 250 Infrastructure
(billion kWh/year)
Electricity use
200 Communications
• Energy optimization of future 150 Storage
data centers will require a 100 High-end servers
50 Mid-range servers
global and multi-disciplinary 0 Volume servers
approach. 2000 2005 2010
José M.Moya | Madrid (Spain), July 27, 2012 8
9. CAMPUS OF Temperature-dependent
INTERNATIONAL
EXCELLENCE reliability problems
“Ingeniamos el futuro”
✔
Electromigration (EM)
✖
Time-dependent
dielectric-
breakdown (TDDB)
Stress
migration (SM)
✖
✖ Thermal
cycling (TC)
José M.Moya | Madrid (Spain), July 27, 2012 9
10. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Cooling a data center
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 10
11. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Server improvements
“Ingeniamos el futuro”
• Virtualization
- 27%
• Energy Star server
conformance
= 6.500
• Better capacity
planning 2.500
José M.Moya | Madrid (Spain), July 27, 2012 11
12. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Cooling improvements
“Ingeniamos el futuro”
• Improvements in air flow management and
wider temperature ranges
Energy savings
up to 25%
25.000
Return of investment
in only 2 years
José M.Moya | Madrid (Spain), July 27, 2012 12
13. CAMPUS OF
INTERNATIONAL Infrastructure improvements
EXCELLENCE
“Ingeniamos el futuro”
AC DC
– 20% reduction of power losses in the
conversion process
– 47 million dollars savings of real-state costs
– Up to 97% efficiency, energy saving enough to
power an iPad during 70 million years
José M.Moya | Madrid (Spain), July 27, 2012 13
14. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Best practices
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 14
15. CAMPUS OF
And…
what about IT people?
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 15
16. CAMPUS OF
PUE
Power Usage Effectiveness
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
• State of the Art: PUE ≈ 1,2
– The important part is IT energy consumption
– Current work in energy efficient data centers is
focused in decreasing PUE
– Decreasing PIT does not decrease PUE, but it is seen in
the electricity bill
• But how can we reduce PIT ?
José M.Moya | Madrid (Spain), July 27, 2012 16
17. CAMPUS OF
Potential energy savings
by abstraction level
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 17
18. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Our approach
“Ingeniamos el futuro”
• Global strategy to allow the use of multiple
information sources to coordinate decisions in order
to reduce the total energy consumption
• Use of knowledge about the energy demand
characteristics of the applications, and
characteristics of computing and cooling resources
to implement proactive optimization techniques
José M.Moya | Madrid (Spain), July 27, 2012 18
19. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Holistic approach
“Ingeniamos el futuro”
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 19
20. CAMPUS OF
1. Room-level resource
INTERNATIONAL
EXCELLENCE management
“Ingeniamos el futuro”
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 20
21. CAMPUS OF
INTERNATIONAL Leveraging heterogeneity
CCGrid 2012
EXCELLENCE
“Ingeniamos el futuro”
• Use heterogeneity to minimize energy
consumption from a static/dynamic point of view
– Static: Finding the best data center set-up, given a
number of heterogeneous machines
– Dynamic: Optimization of task allocation in the
Resource Manager
• We show that the best solution implies an
heterogeneous data center
– Most data centers are heterogeneous (several
generations of computers)
M. Zapater, J.M. Moya, J.L. Ayala. Leveraging Heterogeneity for
Energy Minimization in Data Centers, CCGrid 2012
José M.Moya | Madrid (Spain), July 27, 2012 21
22. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Current scenario
“Ingeniamos el futuro”
Scheduler Resource
WORKLOAD
Manager
Execution
José M.Moya | Madrid (Spain), July 27, 2012 22
23. CAMPUS OF
Potential improvements
with best practices
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
Total power (computing and cooling) for various scheduling approaches
1400 max computing power, worst thermal placement
min computing power, worst thermal placemenit
optimal computing+cooling
1200 optimal computing+cooling, shut off idles
optimal computing+cooling, shut off idles, no recirculation
1000
Power (KW)
savings by minimizing computing power
savings by minimizing the recirculation’s effect
800 savings by turning off idle machines
unaddressed heat recirculation cost
600 basic (unavoidable) cost
400
200
0
0 20 40 60 80 100
job size relative to data center capacity (%)
José
operation cost (in kilowatts) for various “savings
Fig. 3. Data center M.Moya | Madrid (Spain), July 27, 2012 23
24. energy consume
energy consume
20
Cooling-aware scheduling and
100
15
CAMPUS OF
resource allocation
10
INTERNATIONAL
50
EXCELLENCE 5
0
iMPACT Lab (Arizona State U)
0
“Ingeniamos el futuro” FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT
(a) (b)
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off
40 Energy consumption, Scenario (a) 40 jobs, 25014 core-hours, idle servers off
Energy consumption, Scenario (a) 40 jobs, 25014 core-hours,energy
cooling idle servers on cooling energy
computing energy computing energy
40 cooling energy
300 Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 cooling energy
jobs/hr 0.254 jobs/hr 35 computing energy
200 Turnaround time 8.98 hr computing energy
8.98 hr 12.17 hr 8.98 hr 48.49 hr
Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 jobs/hr 0.427 jobs/hr
Alg. runtime 170 ms 186 ms 397 ms 40.8 min 88.6 min 35
250 Energy savings 0.197 jobs/hr
Throughput 0% 0.197 jobs/hr
1.7% 0.172 jobs/hr
4.1% 0.197 jobs/hr
3.6% 0.163 jobs/hr
4.7% 30 Turnaround time
Throughput
8.98 hr
0.197 jobs/hr
8.98 hr
0.197 jobs/hr
12.17 hr
0.172 jobs/hr
8.98 hr
0.197 jobs/hr
17.75 hr
0.163 jobs/hr
Alg. runtime 171 ms 186 ms 397 ms 42 min 100 min
energy consumed (GJ) (GJ)
energy consumed (GJ) (GJ)
Turnaround time 18.41 hr 18.41 hr 20.75 hr 18.41 hr 51.75 hr Turnaround time 18.41 hr 18.41 hr 20.75 hr 18.41 hr 38.02 hr
Alg. runtime 3.4 ms 6.9 ms 213 ms 23 min 40 min 30 Energy savings 0% 4.0% 14.6% 14.2% 15.1%
25 Alg. runtime 3.4 ms 6.9 ms 213 ms 23 min 43 min
energy consumed
energy consumed
150
200
Energy savings 0% 6.2% 8.6% 8.7% 10.2%
Energy savings 0% 11.8% 54.7% 21.8% 60.5%
25
20
150
20
100 15
100 15
10
50
50 10
5
5
0 0
FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT
0 0
FCFS-FF (c)
FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF (d)
FCFS-LRH EDF-LRH FCFS-Xint SCINT
Energy consumption, Scenario (c)(a) jobs, 45817 core-hours, idle servers on
174 Energy consumption, Scenario (c)(b) jobs, 45817 core-hours, idle servers off
174
Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers on
cooling energy Energy consumption, Scenario (b) 120 jobs, 16039 core-hours, idle servers off
cooling energy
computing energy computing energy
450
cooling energy 40 cooling energy
Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr energy
computing 0.561 jobs/hr 100 Throughput computing
0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr energy
0.590 jobs/hr
400 Turnaround time 9.99 hr 9.99 hr 13.39 hr 9.99 hr 61.49 hr
300 Turnaround time 9.99 hrjobs/hr 9.99 hrjobs/hr 13.39 hr
Throughput 0.580 0.580
9.99 hr 65.38 hr
0.349 jobs/hr 0.580 jobs/hr 0.254 jobs/hr 35 Alg. runtime 173 ms 191 ms 346 ms 21 min 147 min
Alg. runtime time 173 ms
Turnaround 8.98 hr 196 ms
8.98 hr 346 ms
12.17 hr 20 min
8.98 hr 142 min
48.49 hr Energy savings 0.0% 7.5% 17.3% 25.7% 41.4%
350 Throughput 0.580 jobs/hr 0.580 jobs/hr 0.349 jobs/hr 0.580 jobs/hr 0.427 jobs/hr
energy consumed (GJ) (GJ)
energy consumed (GJ) (GJ)
Energy savings 170 ms
Alg. runtime 0% 2.5%
186 ms 5.9%
397 ms 9.4%
40.8 min 12.5%
88.6 min 80
250 Energy savings 0% 1.7% 4.1% 3.6% 4.7% 30 Turnaround time 8.98 hr 8.98 hr 12.17 hr 8.98 hr 17.75 hr
300 Alg. runtime 171 ms 186 ms 397 ms 42 min 100 min
energy consumed
energy consumed Energy savings 0% 4.0% 14.6% 14.2% 15.1%
25
250
200 60
200 20
150 40
150
15
100
100
20
10
50
50
5
0 0
FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF FCFS-LRH EDF-LRH FCFS-Xint SCINT
0 0
FCFS-FF (e)
FCFS-LRH EDF-LRH FCFS-Xint SCINT FCFS-FF (f)
FCFS-LRH EDF-LRH FCFS-Xint SCINT
(c) (d)
Fig. 8. Energy comparison of the simulated schemes for the three scenarios. The plots correspond in respective positions to the plots of Figure 7.
Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers on Energy consumption, Scenario (c) 174 jobs, 45817 core-hours, idle servers off
José M.Moya | cooling energy (Spain), July 27, 2012
Madrid 24 cooling energy
policy used in the data center, which enables energy execution as soon as they arrive if the queue is empty and the data
450
computing
job computing energy
Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr 0.561 jobs/hr 100 Throughput 0.892 jobs/hr 0.892 jobs/hr 0.861 jobs/hr 0.892 jobs/hr 0.590 jobs/hr
400
25. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
LSI-UPM
“Ingeniamos el futuro”
Resource
WORKLOAD Manager
(SLURM)
Execution
Profiling and Energy
Classification Optimization
José M.Moya | Madrid (Spain), July 27, 2012 25
26. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
Scenario
“Ingeniamos el futuro”
• Workload:
– 12 tasks from SPEC CPU INT 2006
– Random workload composed by 2000 tasks, divided into
job sets
– Random job set arrival time
• Servers:
José M.Moya | Madrid (Spain), July 27, 2012 26
27. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
Energy profiling
“Ingeniamos el futuro”
Resource
WORKLOAD Manager
(SLURM)
Execution
Profiling and Energy
Classification Optimization
Energy profiling
José M.Moya | Madrid (Spain), July 27, 2012 27
28. CAMPUS OF
INTERNATIONAL Workload characterization
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 28
29. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
“Ingeniamos el futuro”
Optimization
Resource
WORKLOAD Manager
(SLURM)
Execution
Profiling and Energy
Classification Optimization
Energy Minimization:
• Minimization subjected to constraints
• MILP problem (solved with CPLEX)
• Static and Dynamic
José M.Moya | Madrid (Spain), July 27, 2012 29
30. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
“Ingeniamos el futuro”
Static optimization
• Definition of optimal data center
– Given a pool of 100 servers of each kind
– 1 job set from workload
– The optimizer chooses the best selection of servers
– Constraints of cost and space
Best solution:
• 40 Sparc
• 27 AMD
Savings:
• 5 a 22% energy
• 30% time
José M.Moya | Madrid (Spain), July 27, 2012 30
31. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
“Ingeniamos el futuro”
Dynamic optimization
• Optimal workload allocation
– Complete workload (2000 tasks)
– Good enough resource allocation in terms of energy (not
the best)
– Run-time evaluation and optimization
Energy savings
ranging from 24%
to 47%
José M.Moya | Madrid (Spain), July 27, 2012 31
32. CAMPUS OF Application-aware scheduling and
INTERNATIONAL
EXCELLENCE resource allocation
“Ingeniamos el futuro”
Conclusions
• First proof-of-concept regarding the use of
heterogeneity to save energy
• Automatic solution
• Automatic processor selection offers notable energy
savings
• Easy implementation in real scenarios
– SLURM Resource Manager
– Realistic workloads and servers
José M.Moya | Madrid (Spain), July 27, 2012 32
33. CAMPUS OF
2. Server-level resource
INTERNATIONAL
EXCELLENCE management
“Ingeniamos el futuro”
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 33
34. CAMPUS OF Scheduling and resource allocation
INTERNATIONAL
EXCELLENCE policies in MPSoCs
“Ingeniamos el futuro”
UCSD – System Energy Efficiency Lab
A. Coskun , T. Rosing , K. Whisnant and K. Gross "Static and dynamic temperature-
aware scheduling for multiprocessor SoCs", IEEE Trans. Very Large Scale Integr. Syst.,
vol. 16, no. 9, pp.1127 -1140 2008
Fig. 3. Distribution of thermal hot spots, with with DPM (ILP).
Fig. 3. Distribution of thermal hot spots, DPM (ILP). Fig. 4. Distribution of spatial gradients, with with DPM (ILP).
Fig. 4. Distribution of spatial gradients, DPM (ILP).
A. Static Scheduling Techniques
A. Static Scheduling Techniques hot spots. While Min-Th reduces the spatial differentials
hot spots. While Min-Th reduces the highhigh spatial differentials
We We next provideextensive comparison of the ILP ILP based above 15 we observe a substantial increase in the spatial
next provide an an extensive comparison of the based above 15 C, C, we observe a substantial increase in the spatial
José M.Moya | Min-Th&Sp. gradients
techniques. We refer to to static approach as as Madrid (Spain), July 27, 2012 above C. C. In contrast, method achieves lower
techniques. We referour our static approach Min-Th&Sp. gradients above 10 10 In contrast,34 our method achieves lower
our
As discussedSection III, we implemented the ILP ILP min- and and more balanced temperature distribution in die. die.
As discussed in in Section III, we implemented the for for min- more balanced temperature distribution in the the
35. CAMPUS OF Scheduling and resource allocation
INTERNATIONAL
EXCELLENCE policies in MPSoCs
“Ingeniamos el futuro”
• Energy characterization of applications allows
to define proactive scheduling and resource
allocation policies, minimizing hotspots
• Hotspot reduction allows to raise cooling
temperature
+1oC means around 7% cooling energy savings
José M.Moya | Madrid (Spain), July 27, 2012 35
36. CAMPUS OF
3. Application-aware and
INTERNATIONAL
EXCELLENCE resource-aware virtual machine
“Ingeniamos el futuro”
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 36
37. CAMPUS OF
JIT compilation in
virtual machines
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
• Virtual machines compile
(JIT compilation) the
applications into native
code for performance
reasons
• The optimizer is general-
purpose and focused in
performance
optimization
José M.Moya | Madrid (Spain), July 27, 2012 37
38. CAMPUS OF
JIT compilation for
energy minimization
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
Back-end
Front-end Code generator
Optimizer
• Application-aware compiler
– Energy characterization of applications and
transformations
– Application-dependent optimizer
– Global view of the data center workload
• Energy optimizer
– Currently, compilers for high-end processors oriented
to performance optimization
José M.Moya | Madrid (Spain), July 27, 2012 38
39. CAMPUS OF
Energy saving potential for
the compiler (MPSoCs)
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
T. Simunic, G. de Micheli, L. Benini, and M. Hans. “Source code optimization and
profiling of energy consumption in embedded systems,” International Symposium on
System Synthesis, pages 193 – 199, Sept. 2000
– 77% energy reduction in MP3 decoder
FEI, Y., RAVI, S., RAGHUNATHAN, A., AND JHA, N. K. 2004. Energy-optimizing source
code transformations for OS-driven embedded software. In Proceedings of the
International Conference VLSI Design. 261–266.
– Up to 37,9% (mean 23,8%) energy savings in
multiprocess applications running on Linux
José M.Moya | Madrid (Spain), July 27, 2012 39
40. CAMPUS OF
4. Global automatic
INTERNATIONAL
EXCELLENCE
management of low-power
“Ingeniamos el futuro”
modes
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 40
41. CAMPUS OF
DVFS – Dynamic Voltage
and Frequency Scaling
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
• As supply voltage decreases, power decreases
quadratically
• But delay increases (performance decreases)
only linearly
• The maximum frequency also decreases
linearly
• Currently, low-power modes, if used, are
activated by inactivity of the server operating
system
José M.Moya | Madrid (Spain), July 27, 2012 41
42. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Room-level DVFS
“Ingeniamos el futuro”
• To minimize energy consumption, changes
between modes should be minimized
• There exist optimal algorithms for a known
task set (YDS)
• Workload knowledge allows to globally
schedule low-power modes without any
impact in performance
José M.Moya | Madrid (Spain), July 27, 2012 42
43. CAMPUS OF
INTERNATIONAL Parallelism to save energy
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 43
44. CAMPUS OF 5. Temperature-aware floorplanning of
INTERNATIONAL
EXCELLENCE MPSoCs and many-cores
“Ingeniamos el futuro”
Chip Server Rack Room Multi-
room
Sched & alloc 2 1
app
OS/middleware
Compiler/VM 3
architecture 4 4
technology 5
José M.Moya | Madrid (Spain), July 27, 2012 44
45. CAMPUS OF
Temperature-aware
floorplanning
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
José M.Moya | Madrid (Spain), July 27, 2012 45
46. Average MaxTemp reduction: 12 oC
Potential energy savings
CAMPUS OF
Larger temperature reductions for benchmarks
with floorplanning
INTERNATIONAL
with higher maximum temperature
EXCELLENCE
“Ingeniamos el futuro”
For many benchmarks, temperature reducions are
Y. Han, I. Koren, and C. A. Moritz. Temperature Aware Floorplanning. In Proc. of the
larger than 20 oC
Second Workshop on Temperature-Aware Computer Systems, June 2005
Maximum Temperature original modified
140
120
100
80
60
40
20
0
wupwise
twolf
swim
gzip
mgrid
mcf
lucas
applu
ammp
bzip2
crafty
fma3d
perlbmk
vortex
avg
apsi
vpr
equake
facerec
gcc
mesa
eon
gap
art
parser
– Up to 21oC reduction of maximum temperature
– Mean: -12oC in maximum temperature
– Better results in the most critical examples
José M.Moya | Madrid (Spain), July 27, 2012 46
47. CAMPUS OF
Temperature-aware
INTERNATIONAL
EXCELLENCE floorplanning in 3D chips
“Ingeniamos el futuro”
• 3D chips are getting interest due to:
– Scalability: reduces 2D equivalent
area
– Performance: shorter wire length
– Reliability: less wiring
• Drawback:
– Huge increment of hotspots
compared with 2D equivalent designs
José M.Moya | Madrid (Spain), July 27, 2012 47
48. CAMPUS OF
Temperature-aware
floorplanning in 3D chips
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
• Up to 30oC reduction per layer in a 3D chip
with 4 layers and 48 cores
José M.Moya | Madrid (Spain), July 27, 2012 48
49. CAMPUS OF
There is still much more
to be done
INTERNATIONAL
EXCELLENCE
“Ingeniamos el futuro”
• Smart Grids
– Consume energy when everybody else does not
– Decrease energy consumption when everybody
else is consuming
• Reducing the electricity bill
– Variable electricity rates
– Reactive power coefficient
– Peak energy demand
José M.Moya | Madrid (Spain), July 27, 2012 49
50. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Conclusions
“Ingeniamos el futuro”
• Reducing PUE is not the same as reducing energy
consumption
– IT energy consumption dominates in state-of-the-art data
centers
• Application and resources knowledge can be effectively
used to define proactive policies to reduce the total energy
consumption
– At different levels
– In different scopes
– Taking into account cooling and computation at the same time
• Proper management of the knowledge of the data center
thermal behavior can reduce reliability issues
• Reducing energy consumption is not the same as reducing
the electricity bill
José M.Moya | Madrid (Spain), July 27, 2012 50
51. CAMPUS OF
INTERNATIONAL
EXCELLENCE
Contact
“Ingeniamos el futuro”
José M. Moya
+34 607 082 892
jm.moya@upm.es
ETSI de Telecomunicación, B104
Avenida Complutense, 30
Madrid 28040, Spain
Gracias:
José M.Moya | Madrid (Spain), July 27, 2012 51