DC4Cities: Incresing Renewable Energy Utilization of Data Centre in a smart cities. Page 1

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1 DC4Cities: Incresing Renewable Energy Utilization of Data Centre in a smart cities M A RTA CHINNICI R E S E A R C H E R E N E A Page 1

2 DC4Cities Origins Green Service Level Agreements To Adapt the Renewable Energy Availability Virtual Machine Dynamic Consolidation and Turn off Servers Page 2

3 DC4Cities DC4Cities: An environmentally sustainable data centre for Smart Cities FP7-SMARTCITIES-13 (ICT Call) Objective ICT Data Centres in an energy-efficient and environmentally friendly Internet The results of the project research will be The goal of DC4Cities evaluated is in to two make (already any type existing) of existing Smart City and new DCs energy adaptive: trial test beds in Trento (Italy) and in - The project will Barcelona find the (Spain), adequate and actions by special to run lab a DC so as to adapt to external energy experimentation constraints such at the ashp renewable Italy Innovation energy availability and consume the minimal energy, Centre. without requiring any modification to the logistics, and without impacting the quality of the services provided to their users. In addition new energy metrics, benchmarks, and measurement methodologies will be developed and proposed for the definition of new related standards. Page 3

of existing Smart City and new DCs energy adaptive: trial test beds in Trento (Italy) and in - The project will Barcelona find the (Spain), adequate and actions by special to run lab a DC so as to

4 DC4Cities Web Site and Partners Page 4

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 Certain Services require DCs to be close to users Smart Cities require Services, hosted by DCs Smart Cities need Ecofriendly DCs Europe needs new metrics for DCs energy efficiency A usage of 80 % of renewable energy sources in DCs is targeted while, at the same time, the DCs energy consumption is minimized. Page 5

.. running a data centre at high levels of locally renewable energy sources is the great challenge Certain Services require DCs to be close to

6 DC4Cities Main Goal The challenge for eco-friendly data centres is the maximization of their utilization of locally 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 6

Thus, these data centres need to: optimize their overall energy consumption, to reduce absolute energy footprint dynamically adapt energy

7 DC4Cities Concept 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 received by the Smart City Energy Management authority DC4Cities controls generates PUE* Hardware Software Power Execution Load Consumption Data Centre Total Power Consumption controls * approximate factor Page 7

received by the Smart City Energy Management authority DC4Cities controls generates PUE* Hardware

8 DC4Cities Impact on Renewable Energy Utilization Power Trad. DC Renewable Power Power DC4Cities Renewable Power Time Time 50% DC not using Renewable Power % DC not using Renewable Power 50% DC using Renewable Power 80% DC using Renewable Power Page 8

50% DC not using Renewable Power % DC not using Renewable

9 DC4Cities Overview 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 Grid/Smart Grid Ren Energy Providers Smart City Control WP4 WP3 WP5 WP2 Renewable Energy Adaptive Interface Data Centre Energy Controller Energy Adaptive Data Centre Operation Interface User and Admin Task Scheduling Infr. Mgmt Energy Adaptive SW WP6 Page 9

The Energy Adaptive Data Centre Operation Interface is used to enact power consumption plans on the data centers subsystems Grid/Smart Grid Ren Energy Providers Smart City

10 3:00 6:00 9:00 15:00 18:00 21:00 2:00 4:00 6:00 8: :00 16:00 18: :00 4:00 8:00 16:00 2 DC4Cities Overview (Nord-level) Grid/Smart Grid Renewable Energy Providers Smart City Control Sun Wind Gas Coal Energy Availability Forecast RenPct Weather Forecast PV power Power/ Energy Goals 80% Ren Renewable Energy Adaptive Interface Data Centre Energy Controller Page 10

100 80 60 40 0 Sun Wind Gas Coal Energy Availability Forecast 60 40 0 RenPct Weather Forecast 40 30 10 0

11 2:00 4:00 6:00 8: :00 16:00 18: :00 2:00 4:00 6:00 8: :00 16:00 18: :00 2:00 4:00 6:00 8: :00 16:00 18: :00 2:00 4:00 6:00 8: :00 16:00 18: :00 3:00 6:00 9:00 15:00 18:00 21:00 DC4Cities Overview (Control) -Get Energy Forecast(s) 80 -Compute Max/Ideal Power 60 Plan 40 -Split Power Plan into Quotas for EA SW -Use Power Splitter Policies 0 -Send Power Quotas and collect power plan options from EA SW controllers Data Centre Energy Controller Renewable Energy Adaptive Interface DC Ideal power Serv C Serv B Serv. A Service Quota Split Policies Quota B Quota A Quota C Energy Adaptive Data Centre Operation Interface Page 11

for EA SW -Use Power Splitter Policies 0 -Send Power Quotas and collect power plan options from EA SW controllers Data Centre Energy Controller Renewable Energy Adaptive

12 Green SLAs (All4Green) User and Admin Task Scheduling Infrastructure Mgmt Energy Adaptive SW HighPerf W3D2 100% ECU MidPerf 10000pg/m W2D2 75% ECU LowPerf 6000pg/m W1D1 3000pg/m Page 12

13 3:00 6:00 9:00 15:00 18:00 21:00 3:00 6:00 9:00 15:00 18:00 21:00 3:00 6:00 9:00 15:00 18:00 21:00 2:00 4:00 6:00 8: :00 16:00 18: :00 DC4Cities Overview (Control - South) Renewable Energy Adaptive Interface Smart City Energy Admin Data Centre Energy Admin % C2 B2 A1 -Get EA SW Power Plan Option(s) -Consolidate DC Power Plan Options (solution) -Send Power Plan Option to execute EA SW Ctrls -Exec EA SW Power Plan Option B2 A1 C B1 A B2 0 A2 0 C1 C2 C3 Energy Adaptive Data Centre Operation Interface Send DC Power Plan Page 13

80% C2 B2 A1 -Get EA SW Power Plan Option(s) -Consolidate DC Power Plan Options (solution) -Send Power Plan Option to execute EA SW Ctrls -Exec

14 Cloud Front End DC4Cities IaaS CTRL and VM Placement Optimizer (FIT4Green) Energy Adaptive Software Control Interface Monitoring & Forecast EA SW CTRL IaaS Change SLA value(s): VM consolidation factor Infrastructure Mgmt End Users Green SLA: Time/Calendar Power Status Deploy option EA SW Profile: Deploy options Actions Elasticity FIT4Green VM placement optimization Assign/Migrate VMs to/between servers Create Use Delete VMs Page 14

DC Federation Control DCs federation: DC4Cities will capitalize on a federation of small to medium size city data centres that can combine the advantages of being exactly where the computing needs

15 DC Federation Control DCs federation: DC4Cities will capitalize on a federation of small to medium size city data centres that can combine the advantages of being exactly where the computing needs are higher, minimizing the network latency, 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 Page 15

minimizing the network latency, offering more security and redundancy 2 LEVELS: 1.

16 Metrics and Benchmarks Page 16

17 Contribution to metrics/ standards 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, in order to outline a standardization procedure. Propose new metrics: 1. Software Execution Energy Efficiency: Amount of services delivered by the DC with respect to the consumed energy 2.Renewable Energy Utilization Efficiency: The effective utilization of the available renewable energy Research specific benchmarks 1. to support the measurement of energy efficacy 2. to allow comparison of the results among different DCs. New metrics, based on the ratio between: (Useful work produced)/(total energy consumed to produce that work) Page 17

Software Execution Energy Efficiency: Amount of services delivered by the DC with respect to the consumed energy 2.

18 Contribution to metrics/ standards Real Workloads or Benchmarks Trigger = Work Requests Input = Renewable Energy Input = Non-Renewable Energy Total Work Done Total Energy Renewable Energy Total Energy Software Execution Energy Efficiency Renewable Energy Utilization Efficiency Output = Work Done Collaboration inside EU Project Cluster for common standardization proposal to CEN-CENELEC-ETSI Coordination Group on Green Data Centres (CG GDC) Page 18

Energy Efficiency Renewable Energy Utilization Efficiency Output = Work Done Collaboration inside EU Project

19 DC4Cities Trials Page 19

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

21 DC4Cities Trials DC4Cities First Trial will run during Summer 14 HP, Milan IT CSUC/IMI Barcelona ES Create-Net, Trento IT Page 21

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! marta.chinnici@enea.it Reference: presented at DataCentreEurope14 by Giovanni Giuliani - HP Page 22

Leveraging Renewable Energy in Data Centers: Present and Future

Leveraging Renewable Energy in Data Centers: Present and Future Ricardo Bianchini Department of Computer Science Collaborators: Josep L. Berral, Inigo Goiri, Jordi Guitart, Md. Haque, William Katsak, Kien