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Page 1
Renewable Energy-Aware Data
Centre Operations for Smart Cities
– the DC4Cities Approach
SMARTGREENS 2015
S O N J A ...
Page 2
General Approach
SMARTGREENS 2015
 Data Centres in the City
 Lack of locally produced renewable energy due to
spa...
Page 3
High-Level Architecture
SMARTGREENS 2015
Page 4
Coordination between SC and DC
SMARTGREENS 2015
 A new authority: The energy management
authority of the smart cit...
Page 5
Energy Adaptation within a DC
SMARTGREENS 2015
 Multi-level API for IaaS, PaaS and SaaS
Page 6
Incentives and Monitoring
SMARTGREENS 2015
 Smart City as mediator between Energy System and
DCs
 RenEnergy Contr...
Page 7
The DC4Cities Architecture
SMARTGREENS 2015
1. DC4cities process controller retrieves the next 24 hours energy
fore...
Page 8
DC4Cities - Trials
SMARTGREENS 2015
CPU Intensive
video
conversion
task
Generation of
Reports for
local health
syst...
Page 9
Results – HP and Trento
SMARTGREENS 2015
 Batch jobs: Producing 4320 reports per day
 Percentage of Renewable Ene...
Page 10
Results –HP and Trento (cont.)
SMARTGREENS 2015
 When adding 8 local solar panels (max 250Wh) to the
previous set...
Page 11
Q U E S T I O N S ?
SMARTGREENS 2015
Thank you!
K L I N G E R T @ I N F O R M A T I K . U N I - M A N N H E I M . ...
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Renewable Energy-Aware Data Centre Operations for Smart Cities - The DC4Cities Approach

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Presentation held by Sonja Klingert, from University of Mannheim, at the SMARTGREENS 2015 in Lisbon (Portugal) on 20-22 May.

Veröffentlicht in: Technologie
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Renewable Energy-Aware Data Centre Operations for Smart Cities - The DC4Cities Approach

  1. 1. Page 1 Renewable Energy-Aware Data Centre Operations for Smart Cities – the DC4Cities Approach SMARTGREENS 2015 S O N J A K L I N G E R T U N I V E R S I T Y O F M A N N H E I M D C 4 C I T I E S g r o u p Follow us! @ D C 4 C I T I E S
  2. 2. Page 2 General Approach SMARTGREENS 2015  Data Centres in the City  Lack of locally produced renewable energy due to space limitations. -> minimize energy consumption and adhere to constraints of a higher directive – the EMA-SC
  3. 3. Page 3 High-Level Architecture SMARTGREENS 2015
  4. 4. Page 4 Coordination between SC and DC SMARTGREENS 2015  A new authority: The energy management authority of the smart city (EMA-SC)  The EMA-SC sets objectives to which the data centres have to adhere to  These are taken into account for calculating an ideal power budget in the DC  In case the DC cannot comply with the objectives an escalation to the EMA is triggered
  5. 5. Page 5 Energy Adaptation within a DC SMARTGREENS 2015  Multi-level API for IaaS, PaaS and SaaS
  6. 6. Page 6 Incentives and Monitoring SMARTGREENS 2015  Smart City as mediator between Energy System and DCs  RenEnergy Contract between EMA-SC and DC  DCAdapt metric: Deviation between Ideal Power Plan and realized power profile  RenPercent metric: The share of renewable energy consumed by the DC  GreenSLAs: Contracts between DCs and it‘s costumers allowing  more flexibility and  can contain metrics describing the guarenteed eco-efficiency of the service
  7. 7. Page 7 The DC4Cities Architecture SMARTGREENS 2015 1. DC4cities process controller retrieves the next 24 hours energy forecasts for each EP of the DC through the ERDS handler 2. The Max/Ideal power plan is computed3. The power plan is split into different plans, one for each service hosted by the DC 4. Multiple splitting policies can be configured to better tailor the system to the DC business needs 5. The controller will request EASC to create specific power budgets for the next 24 hours for each service 6. The Option plan collector will receive a set of possible alternatives by each EASC 7. All Option plans will be consolidated and globally optimized to achieve the best usage of renewable energy source 8. If a good solution is found, the EASCs are informed which option plan to enact. Else, an escalation process is triggered [8x] 9. EASC will use automation tools to control the SW/HW resources of the service in line with the received plan (Working Mode). 10. Finally the controller will share the DC power plan with the energy provider, to enable some form of demand/response cooperation
  8. 8. Page 8 DC4Cities - Trials SMARTGREENS 2015 CPU Intensive video conversion task Generation of Reports for local health system Test Lab for a web E-learning platform (worldwide)
  9. 9. Page 9 Results – HP and Trento SMARTGREENS 2015  Batch jobs: Producing 4320 reports per day  Percentage of Renewable Energy in the Italian Grid varies between 29,21% and 49,18% (avg. 37,16) Uniform workload distribution over 24 hours Workload concentrated at grid max RenPerc 37,16% 42,20%
  10. 10. Page 10 Results –HP and Trento (cont.) SMARTGREENS 2015  When adding 8 local solar panels (max 250Wh) to the previous setting, the RenPercent rises to 79,41% Local Solar Energy Production
  11. 11. Page 11 Q U E S T I O N S ? SMARTGREENS 2015 Thank you! K L I N G E R T @ I N F O R M A T I K . U N I - M A N N H E I M . D E W W W . D C 4 C I T I E S . E U D C 4 C I T I E S g r o u p Follow us! @ D C 4 C I T I E S Contact us!

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