DC4Cities: an innovative approach for efficient and environmentally sustainable Data Centre for Smart Cities

NOVEMBER 18, 2015
DC4Cities: an innovative approach for
efficient and environmentally sustainable
Data Centre for Smart Cities
DC4Cities @ Smart City Expo

DC4Cities Origins
DC4Cities @ Smart City Expo Centers
Virtual Machine
Dynamic
Consolidation and
Turn off Servers
Green Service
Level Agreements
Adapt to Renewable
Energy Availability
Sep 2013 - Feb 2016Nov 2011- Apr 2014Jan 2010- Jun 2012
HP Labs
NetZero
Parasol

DC4Cities Rationale
Running a data centre at high levels of
renewable energy sources is the great
challenge
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
DC energy
efficiency
DC4Cities:
an environmentally sustainable data centre for Smart
Cities
FP7-SMARTCITIES-2013 (ICT Call) Objective ICT-2013.6.2
Data Centres in an energy-efficient and environmentally friendly Internet

DC4Cities Concept
DC4Cities: let DCs become energy adaptive
Eco-friendly DC energy policies needs to be capable of
adapting the power
consumption to the availability
of renewable energy
being adapted to the requests
received by the Smart City
Energy Management authority
Trad.
DC
Power
Renewable
Power
Time
50%
50%
DC not using Renewable Power
DC using Renewable Power
DC4Cities
Power
Renewable
Power
Time
20%
80%
DC not using Renewable Power
DC using Renewable Power

DC4Cities Web Site and Partners
http://www.dc4cities.eu

Data Centre
Energy
Controller
User and Admin Task Scheduling Infrastructure Mgmt Energy Adaptive SW
Renewable Energy Adaptive Interface
Energy Adaptive Data Centre Operation Interface
Grid/Smart Grid Renewable Energy Providers Smart City Control
DC4Cities Overview
WP3
WP5
WP4
WP6
WP2

DC4Cities System
North-bound
SubSystem
South-bound
SubSystem
D4C Control SubSystem
ERDS - Energy/Power Forecasts Data Environmental Data
Renewable Energy Forecasting Interface
Energy Adaptive Software Control Interface
Connector
Connector
Local Forecaster
EA SW Ctrl 1 EA SW Ctrl n
SW 1 SW nEASW
DC4Cities Interfaces

Data Centre
Energy
Controller
Grid/Smart Grid Renewable Energy Providers Smart City Control
DC4Cities Overview (North)
0
20
40
60
80
100
120
140
160
0:00
4:00
8:00
12:00
16:00
20:00
0:00
Sun
Wind
Gas
Coal
Energy
Availability
Forecast
Weather
Forecast
Power/
Energy
Goals
80
%
Ren
0
20
40
60
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
RenPct
0
10
20
30
40
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
PV power

Data Centre
Energy
Controller
DC4Cities Overview (Control)
0
20
40
60
80
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
DC Ideal power
0
20
40
60
80
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
Serv C
Serv B
Serv. A
0
10
20
30
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
Quota A
0
10
20
30
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
Quota B
0
10
20
30
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
Quota C
Service
Quota
Split
Policies

Working Modes: Admin Task
 Working mode A : fast
scan = 10000 files/hour
 5 Anti Virus Scanners
 Working mode B : reg
scan= 4000 files/hour
 Working mode C : slow
scan= 2000 files/hour
 System has 20000 files to
scan (work to do)
A A
A
B BB
BB
B B
time
time
time
C
Various
Option
Plans

Data Centre
Energy
Admin
DC4Cities Overview(Control/S)
0
5
10
15
20
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
A1
A20
10
20
30
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
B1
B2 0
10
20
30
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
C1
C2
C3
0
10
20
30
40
50
60
0:00
2:00
4:00
6:00
8:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
0:00
C2
B2
A1
81%
B2 A1 C2
Smart CityEnergy Admin

Federated Services
Service instances are FLAGGED at configuration
level as:
 Deploy ONLY on DCx [Ex. A, D]
 Service can run only inside one DC, can’t be
moved/relocated  “not migratable”
 Deploy on DCx OR DCy (eXclusive OR) [Ex. B]
 Service can potentially run on multiple DCs, but only one
instance running at each time slot, i.e. only one 1 DC at the
time  “migratable” (select before start | stop&restart elsewhere)
 Deploy on DCx AND/OR DCy [Ex. C]
 Service can potentially run on multiple DCs, multiple
instances running at each time slot, even in multiple DCs at
the time  “spreadable”

Assisted/On demand
Federation
!
Data
Centre 1
Manager
80%
?
Smart City Control
?
Data
Centre 2
Manager
DC 1 Operators DC 2 OperatorsTechnical Aspects of
Service Relocation
EASCEASC EASC
EASC EASC EASC
Migration
Candidates
(Configuration)
Best
Migration
Candidate(s)
D4C
dashboard
tools
(optional)
D4C
dashboard
tools

84% 87%
Data
Centre 1
Manager
Data
Centre 2
Manager
Smart City Control
EASCEASC EASC EASCEASC EASC
Power Plan Splitter
Power Plan
Consolidator
EASCEASC EASC EASCEASC EASC
Power Plan Splitter
Continuous Federation

Proposal for New Metrics
Real Workloads
or 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
Collaboration inside EU Project Cluster for common standardization proposal to CEN-
CENELEC-ETSI Coordination Group on Green Data Centres (CG GDC)

DC4Cities Trials
Trento

WP6 – Barcelona Trial
Cloud Lab Platform
DC4CITIES MV
Interacts with:
- ONE controller
- Zabbix [API]
- VM’s [bizperf]
- ILO’s (IPMI)
CSUC IMI

WP6 – Barcelona Trial
(CSUC & IMI)
Trial Scenario : Video transcoding
 At CSUC there are several digital
repositories of content in which
among other content Videos are
uploaded and broadcasted
 To broadcast this videos a
transcoding process is done in order
to reduce and standarized the size
and resolution
1. Diposit
Video metadata
Submit Form
Validation process
DSpace
Preservation
version
Thumbnail
Item
2. Conversion
Media Converter
Broadcast
version
Media Server
3. Broadcast

Trial Barcelona
CSUC results & conclusions
 Main results
- Improvements in RenPercent are lower than expected due to 2 very differentiated
optimization approaches.
Optimization approach 1 Optimization approach 2
Very relevant increase in Ren
Percent.
Percentage improvements vary in the
range 7% -20 %, depending on the
RES availability.
Daily average values near from
the %RES maximum in the grid.
No increase in %RES usage.

 KPIs obtained for each day, and for the trial duration:
RenPercent has increased significantly, and APC values are over 0.9.
 DCeP values obtained, compared to the baseline situation.
 Conclusions:
 As in the case of CSUC, there has been an improvement in RenPercent.
 High flexibility achieved (DCA) and high accuracy following power plan (APC).
 Also, there is a very relevant improvement in energy efficiency terms (DCeP)
Trial Barcelona
IMI results & conclusions

HP Italy Solar Lab
~2KW PV Array
HP Italy HQ
Milan (Italy)
DC/AC Inverter
Meters &
Data Loggers HP Internal
Power Grid
Moonshot
at HP Italy
Technology
Show Room
External
Energy
Provider

HP Moonshot
 HP Moonshot modern
server architecture with
low power consumption
 Radically new system design with
1,000’s of servers per rack—
significantly lowering complexity
and TCO
 Application-tuned configurations
delivering best-of-breed
performance per watt
www.hp.com full web site
From
• 46 Legacy servers
• 115k Watts
To
• 6 Moonshot systems
• 6k watts
ILO with power meter for chassis,
network switches and for each
one of the cartridges (up to 45)
100%
www.hp.com
300M
hits per day
94%
Less power
89%
Less space
(1)
100%
ftp.hp.com

HP LIFE
e-Learning for entrepreneurs
A best-in-class IT and business
skills solution – free and
online
 Supporting individuals worldwide
to start up and run successful
enterprises
 Students
 The unemployed and
underemployed
 Micro- and small-business owners
 Mid-career changers
 HP LIFE is a world wide program
by Hp Corporate Affairs
 IT platform Architecture and
Operations by HP TSC Italy
 Over 500’000 students
(@February 15th, 2015)
www.life-global.org
• Supporting facilitators to
strengthen their services using HP
LIFE e-Learning
 Integration in curriculum
 Career planning and
development
 Lifelong learning
 A condition of a micro-loan

HP Italy Solar Lab
HP
Moonshot
avoided CO2-emission total: 1554 kg (March 18th 2014 – March 17th 2015)

Phase 1 HP Trial Results
Metric DC4Cities vs. Baseline
Min / Max
RenPercent +3.98% / +6.05%
Total Power
Consumption
-13.14% / -13.60%
Service Power
Efficiency
+12.19% / +16.87%
HP Moonshot
17x M300 Cartridges
Power ~500 Watt

For more info
Smart City Expo 2014 @ BCN
http://www.dc4cities.eu
@DC4Cities
DC4Cities Group

DC4Cities: an innovative approach for efficient and environmentally sustainable Data Centre for Smart Cities

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