22
Page 1 MARTA CHINNICI RESEARCHER ENEA DC4Cities: Incresing Renewable Energy Utilization of Data Centre in a smart cities
Page 1
M ARTA CHINNICIRESEARCHER
ENEA
DC4Cities: Incresing Renewable Energy Utilization of
Data Centre in a smart cities
Page 2
DC4Cities Origins
E^DC2014_Cambridge
Virtual Machine Dynamic Consolidation
and Turn off Servers
Green Service Level Agreements
To Adapt the Renewable Energy
Availability
Page 3
DC4Cities
E^DC2014_Cambridge
The goal of DC4Cities is to make any type of existing and new DCs energyadaptive:- The project will find the adequate actions to run a DC so as to adapt to
external energy constraints such as renewable energy availability andconsume the minimal energy, without requiring any modification to thelogistics, and without impacting the quality of the services provided totheir users.
In addition new energy metrics, benchmarks, and measurementmethodologies will be developed and proposed for the definition of new
related standards.
DC4Cities: An environmentally sustainable data centre for Smart
CitiesFP7-SMARTCITIES-2013 (ICT Call) Objective ICT-2013.6.2
Data Centres in an energy-efficient and environmentally friendly Internet
The results of the project research will be evaluated in two (already existing) Smart City
trial test beds in Trento (Italy) and in Barcelona (Spain), and by special lab
experimentation at the HP Italy Innovation Centre.
Page 4
DC4Cities Web Site and Partners
E^DC2014_Cambridge
http://www.dc4cities.eu
Page 5
DC4Cities Rationale
Running a data centre on 100% renewable energy is not a problem if its energy provider is only using (external) hydro and geothermal sources. But if not...
running a data centre at high levels of locally renewable energy sources is the great challenge
A usage of 80 % of renewable energy sources in DCs is targeted while, at the same time, the DCs’ energy consumption
is minimized.
E^DC2014_Cambridge
Certain Services
require DCs to be close to
users
Smart Cities require
Services, hosted by DCs
Smart Cities need Eco-
friendly DCs
Europe needs new metrics for DCs energy efficiency
Page 6
DC4Cities Main Goal
E^DC2014_Cambridge
The challenge for eco-friendly data centres is the maximization of their utilization oflocally available renewable energy sources. Thus, these data centres need to:
• optimize their overall energy consumption, to reduce absolute energy footprint
• dynamically adapt energy demand to the renewable energy availability
DC4Cities will empower all DC components, including applications, to run in energy adaptive mode:
1. matching the renewable energy availability
2. following the Smart City Energy authority directives on energy usage and plans
Page 7
DC4Cities Concept
E^DC2014_Cambridge
Let DCs become energy adaptive
Eco-friendly DC energy policies need to be capable of
adapting the power consumption to the availability
of renewable energy
adapt to the requests receivedby the Smart City EnergyManagement authority
Software Execution Load
HardwarePower
Consumption
Data Centre Total Power
ConsumptionDC4Cities
controls generates PUE*
controls
* approximate factor
Page 8
DC4Cities Impact on Renewable Energy Utilization
Trad.
DC
Power
Renewable
Power
Time
DC4Cities
Power
Renewable
Power
Time
50%
50%
20%
80%
DC not using Renewable Power
DC using Renewable Power
DC not using Renewable Power
DC using Renewable Power
E^DC2014_Cambridge
Page 9
Data Centre
Energy
Controller
User and Admin Task Scheduling Infr. Mgmt Energy Adaptive SW
Renewable Energy Adaptive Interface
Energy Adaptive Data Centre Operation Interface
Grid/Smart Grid Ren Energy Providers Smart City Control
DC4Cities Overview
E^DC2014_Cambridge
The main central component is
the “Data Centre Energy
Controller” that provides two
main interfaces:
• The “Renewable Energy
Adaptive Interface” is used to
retrieve information on
energy availability from
energy providers and energy
constraint directives from the
Smart City authorities and the
Smart Grid.
• The “Energy Adaptive Data
Centre Operation Interface” is
used to enact power
consumption plans on the
data centers’ subsystems
WP2
WP4
WP5
WP3WP6
Page 10
Data Centre
Energy
Controller
Renewable Energy Adaptive Interface
Grid/Smart Grid Renewable Energy Providers Smart City Control
DC4Cities Overview (Nord-level)
E^DC2014_Cambridge
020406080
100120140160
0:0
0
4:0
0
8:0
0
12:0
0
16:0
0
20
:00
0:0
0
Sun
Wind
Gas
Coal
Energy
Availability
Forecast
Weather
ForecastPower/
Energy
Goals
80%Ren
0
20
40
60
0:0
0
3:0
0
6:0
0
9:0
0
12:0
0
15:0
0
18:0
0
21:
00
0:0
0
RenPct
0
10
20
30
40
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
PV power
Page 11
Renewable Energy Adaptive Interface
Energy Adaptive Data Centre Operation Interface
DC4Cities Overview (Control)
E^DC2014_Cambridge
0
20
40
60
80
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
DC Ideal power
0
20
40
60
80
0:0
0
3:0
0
6:0
0
9:0
0
12:0
0
15:0
0
18:0
0
21:
00
0:0
0
Serv C
Serv B
Serv. A
0102030
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
Quota A
0102030
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
Quota B
0102030
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
Quota C
ServiceQuotaSplit
Policies
-Get Energy Forecast(s)
-Compute Max/Ideal Power
Plan
-Split Power Plan into Quotas
for EA SW
-Use Power Splitter Policies
-Send Power Quotas and
collect power plan
options from EA SW controllers
Data Centre
Energy
Controller
Page 12
Green SLAs (All4Green)
E^DC2014_Cambridge
100% ECU
HighPerf
MidPerf
75% ECU
LowPerf
W3D2
W2D2
W1D1
10000pg/m
6000pg/m
3000pg/m
User and Admin Task Scheduling Infrastructure Mgmt Energy Adaptive SW
Page 13
Data Centre
Energy
Admin
Renewable Energy Adaptive Interface
Energy Adaptive Data Centre Operation Interface
DC4Cities Overview(Control - South)
E^DC2014_Cambridge
0
5
10
15
20
0:0
0
3:0
0
6:0
0
9:0
0
12:0
0
15:0
0
18:0
0
21:
00
0:0
0
A1
A20
10
20
30
0:0
0
3:0
0
6:0
0
9:0
0
12:0
0
15:0
0
18:0
0
21:
00
0:0
0
B1
B2 0
10
20
30
0:0
0
3:0
0
6:0
0
9:0
0
12:0
0
15:0
0
18:0
0
21:
00
0:0
0
C1
C2
C3
0
10
20
30
40
50
60
0:0
0
2:0
0
4:0
0
6:0
0
8:0
0
10:0
0
12:0
0
14:0
0
16:0
0
18:0
0
20
:00
22
:00
0:0
0
C2
B2
A1
80%
B2 A1 C2
Smart City
Energy Admin
-Get EA SW Power Plan Option(s)
-Consolidate DC Power Plan Options
(solution)
-Send Power Plan Option to execute to
EA SW Ctrls
-Exec EA SW Power Plan Option
Send DC Power Plan
Page 14
DC4Cities IaaS CTRL and VM Placement Optimizer (FIT4Green)
EA SWCTRLIaaS
FIT4Green VM
placement optimization
Energy Adaptive Software Control Interface
End
Users
Create
Use
Delete
VMs
Assign/Migrate VMs
to/between servers
Change SLA value(s):
VM consolidation factor
Monitoring
&
Forecast
Clo
ud
Fro
nt
En
d
E^DC2014_Cambridge
Infrastructure
Mgmt
EA SW Profile:•Deploy options
•Actions•Elasticity
Green SLA:•Time/Calendar•Power Status Deploy option
Page 15
DC Federation Control
E^DC2014_Cambridge
DCs federation: DC4Cities will capitalize on a federation of small tomedium size “city data centres” that can combine the advantages of beingexactly where the computing needs are higher, minimizing the networklatency, offering more security and redundancy
2 LEVELS:
1. PEER TO PEER before generated escalation (if it is
impossible to consolidate DC power plan options
OTHERWISE
2. call for help to smart city
E^DC2014_Cambridge
Page 16
Metrics and Benchmarks
E^DC2014_Cambridge
Page 17
Contribution to
metrics/ standards
E^DC2014_Cambridge
New metrics, which will involve the renewable energy resource:• Specific WP (WP7) on energy metrics: establish a set of metrics to compare
measurement processes and assess new energy efficiency indicators for DCs, inorder to outline a standardization procedure.• Propose new metrics:
1. Software Execution Energy Efficiency: Amount of services delivered by theDC with respect to the consumed energy
2.Renewable Energy Utilization Efficiency: The effective utilization of theavailable renewable energy
• Research specific benchmarks1. to support the measurement of energy efficacy2. to allow comparison of the results among different DCs.
New metrics, based on the ratio between: (Useful work produced)/(Total energyconsumed to produce that work)
Page 18
Contribution to
metrics/ standards
Real Workloadsor Benchmarks
Output = Work Done
Total Work Done
Total Energy
Renewable Energy
Total Energy
Software Execution
Energy Efficiency
Renewable Energy
Utilization Efficiency
Trigger =
Work
Requests
Input =
Non-Renewable
Energy
Input =
Renewable
Energy
E^DC2014_Cambridge
Collaboration inside EU Project Cluster for common standardization proposal toCEN-CENELEC-ETSI Coordination Group on Green Data Centres (CG GDC)
Page 19
DC4Cities Trials
E^DC2014_Cambridge
Page 20
HP “Solar” Experiment
~2KW PV Array
HP Italy HQ
Milan (Italy)
DC/AC InverterMeters &
Data Loggers HP Internal
Power Grid
Moonshot
at HP Italy
Technology
Show Room
External
Energy
Provider
E^DC2014_Cambridge
Workdone
Page 21
DC4Cities Trials
E^DC2014_Cambridge
DC4Cities First Trial will run during Summer 2014
• HP, Milan IT• CSUC/IMI Barcelona ES• Create-Net, Trento IT
Page 22
T H A N K Y O U F O R Y O U R A T T E N T I O N !
Reference: presented at DataCentreEurope2014 by Giovanni Giuliani - HP
E^DC2014_Cambridge
