Green Software for Renewable Energy-Powered Datacenters

GreenSoftware:
Managing Datacenters Powered by
Renewable Energy
Íñigo Goiri, William Katsak, Md E Haque, Kien Le,
Ryan Beauchea, Jordi Guitart, Jordi Torres,
Thu D. Nguyen, Ricardo Bianchini
Department of Computer Science
Motivation
Datacenters consume large amounts of energy
High energy cost and carbon footprint
Brown electricity: coal and natural gas
Connect datacenters to green sources: solar, wind
Apple DC in Maiden, NC
Apple DC in Maiden, NC
40MW solar farm
40MW solar farm
2
Challenges and opportunities
 
Scheduling workload/energy sources
Lower costs: brown energy, peak brown power, capital
Study opportunities in green datacenters
Build hardware/
software
Power
Time
Load
3
Solar power
Workload
GreenSoftware
 
How to build software for green datacenters?
1.
Malleable energy demand
Idle nodes → Turn off/Sleep (S3) [COLP’01]
Reduce frequency (DVFS) → Lower quality
2.
Move computation under renewables
Weather forecast → Green energy forecast
Delay computation or degrade quality
Leverage energy storage
4
Outline
Motivation
GreenSoftware
GreenSlot
GreenHadoop
GreenSwitch
GreenCassandra
… and others
Conclusion
5
GreenSlot [SC’11]
 
Batch jobs on SLURM (& Hadoop)
Send idle nodes to S3
Predict solar availability
Delay jobs within deadlines
Known jobs characteristics (length, deadline, size…)
Heuristic
6
 
Time
Job 1
 
Power
 
Deadline
Job 2
Job 3
Job 4
GreenSlot [SC’11]
Batch jobs on SLURM (& Hadoop)
Send idle nodes to S3
Predict solar availability
Delay jobs within deadlines
Known jobs characteristics (length, deadline, size…)
Heuristic
7
Time
Job 1
Power
Deadline
Job 2
Job 3
Job 4
GreenHadoop [Eurosys’12]
 
Batch jobs on Hadoop
Send idle nodes to S3
Make required data available
Move data blocks
Predict solar availability
Delay jobs within deadlines
Predict global jobs energy consumption
Heuristic
8
Covering subset
GreenHadoop: Data management
Deactivate servers to save energy
Some data might become unavailable
Prior solution: covering subset [Leverich’09]
Set of servers always running has 
ALL
 data
9
7
3
4
5
2
1
6
8
7
1
4
5
6
3
2
8
1
7
3
Our approach
Only required data has to be available
We usually require fewer active servers
Server
Block
GreenHadoop: Data management
Server 1
1
7
2
Active
Decommission
Down
Required file
Non-required file
JobA
4
JobB
5
JobC
1
6
Running queue:
10
GreenHadoop: Data management
Active
Decommission
Down
GreenHadoop (computation) requires only 2 servers
Required file
Non-required file
JobA
4
JobB
5
JobC
1
6
Running queue:
11
GreenHadoop: Data management
Active
Decommission
Down
Move required files to Active servers
1
 
Replicate
JobA
4
JobB
5
JobC
1
6
Running queue:
12
GreenHadoop: Data management
Active
Decommission
Down
Decommissioned server can be sent to Down
Required file
Non-required file
1
JobA
4
JobB
5
JobC
1
6
Running queue:
13
GreenHadoop: Data management
Active
Decommission
Down
Jobs to be executed change → Required files change
Non-required file
1
 
JobA
4
JobB
5
JobC
1
6
 
JobD
8
Required file
6
4
6
4
6
4
8
 
Required file
Running queue:
14
GreenHadoop: Data management
Active
Decommission
Down
Make missing data available
1
Required file
Non-required file
JobB
5
JobC
1
JobD
8
Required file
Running queue:
15
GreenHadoop: Data management
Active
Decommission
Down
GreenHadoop (computation) requires 3 servers
1
Non-required file
JobB
5
JobC
1
JobD
8
Required file
Running queue:
16
GreenSwitch [ASPLOS’13]
 
Batch jobs on Hadoop
Similar to GreenHadoop
Energy storage
Battery
Net metering
Schedule workload and energy sources
Optimization
Evaluation on Parasol
(Presented on Monday by Thu)
17
GreenCassandra
 
Distributed DB/storage on Cassandra
Add an optional ring
 
 
 
 
 
Degrade quality when no green
18
1
4
6
2
3
5
DHT Ring
A
A
A
Server
Data
GreenSoftware summary
19
Conclusions
Green datacenters
Challenges & opportunities
Hardware/
software
 solution
GreenSoftware
Adapt software to green datacenters
Malleable energy demand
Match computation and renewables
20
GreenSoftware:
Managing Datacenters Powered by
Renewable Energy
Íñigo Goiri, William Katsak, Md E Haque, Kien Le,
Ryan Beauchea, Jordi Guitart, Jordi Torres,
Thu D. Nguyen, Ricardo Bianchini
Department of Computer Science
Other GreenSoftware
GreenSLA [IGCC’13]
Bringing green energy to users
New hardware to route green energy
GreenPar
MPI jobs with sub linear speedup
Use “Free” green energy
GreenNebula
VMs in multiple geo distributed datacenters
Follow the sun
GreenScale
Change frequency (DVFS)
22
GreenPar
MPI jobs on VMs
Add more computation capacity
Lower energy-efficiency
Use available “green”
23
Computation capacity
Speedup
Power
Time
GreenSLA [IGCC’13]
HPC jobs on VMs
Users require % of green energy
Hardware to bring green energy to VMs
Scheduling
Assign green energy to racks
Move VMs among racks
Heuristic
24
Default Green Datacenter
Solar
 
Inverter
PDU
Rack3
PDU
Rack2
PDU
Rack1
Mixed
  Bus
Datacenter
Power
Distribution
GreenSLA:
Proposed Power Distribution
PDU
Rack3
PDU
Rack2
PDU
Rack1
Battery
Mixed
  Bus
Control Module
Charge Controller
Green
  Bus
Solar
 
Inverter
Datacenter
Power
Distribution
Parasol without GreenSwitch
Green use
Green available
Net metering
Brown use
IT load
27
GreenSwitch: deferrable workload
Battery discharge
Battery charge
IT load
Net metering
28
Green available
Green use
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This content delves into the challenges and opportunities associated with managing datacenters powered by renewable energy sources. It explores the motivation behind transitioning datacenters to green energy, outlines techniques for building software tailored to green datacenters, and introduces concepts like GreenSlot and GreenHadoop. The focus is on reducing energy consumption, integrating green energy sources, and optimizing workload scheduling.

  • Green Software
  • Renewable Energy
  • Datacenters
  • Sustainability
  • Technology

Uploaded on Feb 25, 2025 | 0 Views


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  1. GreenSoftware: Managing Datacenters Powered by Renewable Energy igo Goiri, William Katsak, Md E Haque, Kien Le, Ryan Beauchea, Jordi Guitart, Jordi Torres, Thu D. Nguyen, Ricardo Bianchini Department of Computer Science

  2. Motivation Datacenters consume large amounts of energy High energy cost and carbon footprint Brown electricity: coal and natural gas Connect datacenters to green sources: solar, wind Apple DC in Maiden, NC 40MW solar farm 2

  3. Challenges and opportunities Variable Solar power Power Load Workload Time Scheduling workload/energy sources Lower costs: brown energy, peak brown power, capital Study opportunities in green datacenters Build hardware/software 3

  4. GreenSoftware How to build software for green datacenters? 1. Malleable energy demand Idle nodes Turn off/Sleep (S3) [COLP 01] Reduce frequency (DVFS) Lower quality 2. Move computation under renewables Weather forecast Green energy forecast Delay computation or degrade quality Leverage energy storage 4

  5. Outline Motivation GreenSoftware GreenSlot GreenHadoop GreenSwitch GreenCassandra and others Conclusion 5

  6. GreenSlot [SC11] Batch jobs on SLURM (& Hadoop) Send idle nodes to S3 Predict solar availability Delay jobs within deadlines Known jobs characteristics (length, deadline, size ) Heuristic Job 1 Power Job 2 Job 3 Job 4 Time Deadline 6

  7. GreenSlot [SC11] Batch jobs on SLURM (& Hadoop) Send idle nodes to S3 Predict solar availability Delay jobs within deadlines Known jobs characteristics (length, deadline, size ) Heuristic Job 1 Power Job 4 Job 2 Job 3 Time Deadline 7

  8. GreenHadoop [Eurosys12] Batch jobs on Hadoop Send idle nodes to S3 Make required data available Move data blocks Predict solar availability Delay jobs within deadlines Predict global jobs energy consumption Heuristic Shuffle Map 1 Map 2 Reduce 6 Map 3 Reduce 7 Map 4 Map 5 8

  9. GreenHadoop: Data management Deactivate servers to save energy Some data might become unavailable Prior solution: covering subset [Leverich 09] Set of servers always running has ALL data Covering subset Server 7 6 3 2 1 7 1 2 3 6 8 5 7 4 8 3 4 1 5 Block Our approach Only required data has to be available We usually require fewer active servers 9

  10. GreenHadoop: Data management Server 1 Server 2 Server 3 Active 4 7 6 4 1 2 5 3 6 Running queue: Non-required file JobA 4 6 Required file JobB 5 Decommission JobC 1 Server 4 Server 5 Down 2 4 3 6 8 3 7 10

  11. GreenHadoop: Data management Server 1 Server 1 Server 2 Server 3 Active 4 7 7 6 4 1 1 2 2 5 3 6 Running queue: Non-required file JobA 4 6 Required file JobB 5 Decommission JobC 1 Server 4 Server 5 Down 2 4 3 6 8 3 7 GreenHadoop (computation) requires only 2 servers 11

  12. GreenHadoop: Data management Server 2 Server 3 Active 4 1 6 4 5 3 6 Running queue: JobA 4 6 JobB 5 Server 1 Decommission JobC 7 1 1 2 Server 4 Server 5 Down 2 4 3 6 8 3 7 Move required files to Active servers 12

  13. GreenHadoop: Data management Server 2 Server 3 Active 4 1 6 4 5 3 6 Running queue: Non-required file JobA 4 6 Required file JobB 5 Server 1 Server 1 Decommission JobC 7 7 1 1 1 2 2 Server 4 Server 5 Down 2 4 3 6 8 3 7 Decommissioned server can be sent to Down 13

  14. GreenHadoop: Data management Server 2 Server 3 Active 4 4 1 6 6 4 4 5 3 6 6 Running queue: Non-required file JobA 4 6 Required file JobB 5 Decommission JobC 1 JobD 8 Required file Server 1 Server 4 Server 5 Down 2 4 4 7 3 6 6 1 2 8 8 3 7 Jobs to be executed change Required files change 14

  15. GreenHadoop: Data management Server 2 Server 3 Active 4 1 6 4 5 3 6 Non-required file Running queue: Required file JobB 5 Decommission JobC 1 JobD 8 Required file Server 1 Server 4 Server 4 Server 5 Down 2 2 4 4 7 3 6 1 2 8 8 3 3 7 Make missing data available 15

  16. GreenHadoop: Data management Server 2 Server 3 Active 4 1 6 4 5 3 6 Non-required file Running queue: Required file JobB 5 Server 4 Server 4 Decommission JobC 2 2 4 4 1 8 8 3 3 JobD 8 Server 1 Server 5 Down 7 3 6 1 2 7 GreenHadoop (computation) requires 3 servers 16

  17. GreenSwitch [ASPLOS13] Batch jobs on Hadoop Similar to GreenHadoop Energy storage Battery Net metering Schedule workload and energy sources Optimization Evaluation on Parasol (Presented on Monday by Thu) 17

  18. GreenCassandra Distributed DB/storage on Cassandra Add an optional ring Optional 1 1 2 Server 6 2 6 Double DHT Ring DHT Ring 3 5 3 5 A A A A 4 Data A 4 Degrade quality when no green 18

  19. GreenSoftware summary Type Malleable energy Green adaptability Delay jobs Sleep servers GreenSlot Batch jobs Delay until green Delay jobs Sleep servers Data management GreenHadoop Batch jobs Delay until green Delay jobs Sleep servers Delay until green Energy storage GreenSwitch Batch/interactive jobs GreenCassandra Distributed storage Optional ring Degrade quality Migrate VMs Sleep servers Route green energy to racks GreenSLA VMs Change parallelism Sleep servers Greater parallelism on green GreenPar MPI jobs GreenScale Non-deferrable jobs CPU and mem DVFS Faster on green GreenNebula Geo distributed VMs Migrate VMs Follow the renewables 19

  20. Conclusions Green datacenters Challenges & opportunities Hardware/software solution GreenSoftware Adapt software to green datacenters Malleable energy demand Match computation and renewables 20

  21. GreenSoftware: Managing Datacenters Powered by Renewable Energy igo Goiri, William Katsak, Md E Haque, Kien Le, Ryan Beauchea, Jordi Guitart, Jordi Torres, Thu D. Nguyen, Ricardo Bianchini Department of Computer Science

  22. Other GreenSoftware GreenSLA [IGCC 13] Bringing green energy to users New hardware to route green energy GreenPar MPI jobs with sub linear speedup Use Free green energy GreenNebula VMs in multiple geo distributed datacenters Follow the sun GreenScale Change frequency (DVFS) 22

  23. Parasol without GreenSwitch Green available Net metering IT load Green use Brown use 27

  24. GreenSwitch: deferrable workload Green available Net metering Battery charge IT load Battery discharge Green use 28

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