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

1. Page 1
Renewable Energy-Aware Data
Centre Operations for Smart Cities
– the DC4Cities Approach
SMARTGREENS 2015
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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. Page 3
High-Level Architecture
SMARTGREENS 2015

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. Page 5
Energy Adaptation within a DC
SMARTGREENS 2015
 Multi-level API for IaaS, PaaS and SaaS

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. 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. 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. 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. 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. Page 11
Q U E S T I O N S ?
SMARTGREENS 2015
Thank you!
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