energy efficient resource 
management in virtualised 
datacenters 
Fabien Hermenier
USA 3 % 
of the 2005 budget 
(Environmental Protection Agency, 2007) 
1.5% 
in 2010 ?
1Consume less 
2003
the brown constant 
● 
● 
● 
● 
● ● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
● 
180 
175 W 
1...
2003elnozahy et al. : 
“let’s turn off useless stuff” 
Packing concentrate WWW 
requests
2006 Hansen et al. : 
“look! look it’s moving” 
live migration 
N1 VM1 N2
consume less, 
the theory 
1) model power consumption 
2) pack VMs 
3) turn-off unused nodes 
4) minimize(W) 
5) profit
consume less, 
the practice 
hw. heterogeneity 
env. capabilities 
performance vs. energy 
workload volatility 
data cente...
BtrPlace proposal 
core reconfiguration 
algorithm 
users scripts 
+ = 
specialized reconfiguration 
algorithm
spread(VM[2..3]); 
preserve({VM1},’ucpu’, 3); 
offline(@N4); 
The reconfiguration plan 
0’00 
to 
0’02: 
relocate(VM2,N2) ...
the core reconfiguration 
algorithm is modeled wrt. the 
impact of actions on resources
Premadevariables, 
spread({VM1,VM2}): 
allDifferent(dhost 
1 , dhost 
2 ) ^ 
dhost 
1 = chost 
2 ! dst 
1 # ced 
2 ^ 
dhos...
Constraint Programming 
! 
! 
! 
to the rescue
2010 2013 
Energy aware ICT optimization policies 
(+ btrPlace)
multi-core CPUs, 
DDR3 memory, 
spinning HD, 
PUE / CUE, 
boot/shutdown time 
workload particularities 
VM template 
migra...
energy-related variables are linked to 
core ones
energy-oriented constraints 
MaxServerPower 
DelayBtwMigrations 
DelayBtwServerSwitch 
PayBackTime 
SpareCPUs 
minEnergyCo...
Time (minutes) P4G P4G + spare P4G + spare + vcpu P4G + spare + vcpu + delay 
500 1000 1500 2000 2500 
1 
2 
3 
4 
5 
6 
7...
coarse to fine 
grain optimisation 
-16% 
-47% 
- 27% 
-7%
Consuming less 
dealing with 
THE BROWN CONSTANT
Consuming less 
dealing with 
UNAPPROPRIATE 
HARDWARE
energy proportional servers, 
hw. community did not 
chill 
free cooling, 
fanless processors
? workload agnostic 
need to revamp software approaches 
migration 
a balancing problem
Consume be2tter 
2012
DC4Cities 
2013 2016 
let existing and new data centres become energy adaptive
align workload to renewable 
energies availability
forecasts 
1) 24h power 
budget 
2) alt. working 
modes 
energy adaptive 
applications 
3) selected mode 
DC4Cities gray b...
Energy adaptive 
Batch scheduling 
How many parallel jobs, 
which ones to defer 
Trade replicas 
against latency 
Allocate...
ConclusionS
consume less, consume better, non-renewable 
power sources, DVFS, live-migration, 
deferrable workload, so many facets, no...
1.1 - 1.5 % 
in 2010 
USA 
(Environmental Protection Agency, 2013)
.org
energy efficient resource management in virtualised datacenters
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A coarse grain overview of energy-aware resource management approaches since the last ten years through 2 EU founded projects.

Presented during the 2014 "Energy Aware Network" Labex day at Inria. (see http://www.ucnlab.eu/fr/node/66)

Publié dans : Sciences
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energy efficient resource management in virtualised datacenters

  1. 1. energy efficient resource management in virtualised datacenters Fabien Hermenier
  2. 2. USA 3 % of the 2005 budget (Environmental Protection Agency, 2007) 1.5% in 2010 ?
  3. 3. 1Consume less 2003
  4. 4. the brown constant ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 180 175 W 170 160 150 140 130 120 110 0 1 2 3 4 VM # Consumption (Watt) Node consumption statistics (Cluster edel, 128GB HDD) 110 W
  5. 5. 2003elnozahy et al. : “let’s turn off useless stuff” Packing concentrate WWW requests
  6. 6. 2006 Hansen et al. : “look! look it’s moving” live migration N1 VM1 N2
  7. 7. consume less, the theory 1) model power consumption 2) pack VMs 3) turn-off unused nodes 4) minimize(W) 5) profit
  8. 8. consume less, the practice hw. heterogeneity env. capabilities performance vs. energy workload volatility data center size , ,
  9. 9. BtrPlace proposal core reconfiguration algorithm users scripts + = specialized reconfiguration algorithm
  10. 10. spread(VM[2..3]); preserve({VM1},’ucpu’, 3); offline(@N4); The reconfiguration plan 0’00 to 0’02: relocate(VM2,N2) 0’00 to 0’04: relocate(VM6,N2) 0’02 to 0’05: relocate(VM4,N1) 0’04 to 0’08: shutdown(N4) 0’05 to 0’06: allocate(VM1,‘cpu’,3) BtrPlace
  11. 11. the core reconfiguration algorithm is modeled wrt. the impact of actions on resources
  12. 12. Premadevariables, spread({VM1,VM2}): allDifferent(dhost 1 , dhost 2 ) ^ dhost 1 = chost 2 ! dst 1 # ced 2 ^ dhost 2 = chost 1 ! dst 2 # ced 1 constraints
  13. 13. Constraint Programming ! ! ! to the rescue
  14. 14. 2010 2013 Energy aware ICT optimization policies (+ btrPlace)
  15. 15. multi-core CPUs, DDR3 memory, spinning HD, PUE / CUE, boot/shutdown time workload particularities VM template migration duration migration payback time hw. particularities a fine-grain power model
  16. 16. energy-related variables are linked to core ones
  17. 17. energy-oriented constraints MaxServerPower DelayBtwMigrations DelayBtwServerSwitch PayBackTime SpareCPUs minEnergyCons minGasEmissions cap consumption reduce ping-pong effects migration as an investment control the consolidation aggressiveness optimisation criteria
  18. 18. Time (minutes) P4G P4G + spare P4G + spare + vcpu P4G + spare + vcpu + delay 500 1000 1500 2000 2500 1 2 3 4 5 6 7 Servers dominates scalability the core problem
  19. 19. coarse to fine grain optimisation -16% -47% - 27% -7%
  20. 20. Consuming less dealing with THE BROWN CONSTANT
  21. 21. Consuming less dealing with UNAPPROPRIATE HARDWARE
  22. 22. energy proportional servers, hw. community did not chill free cooling, fanless processors
  23. 23. ? workload agnostic need to revamp software approaches migration a balancing problem
  24. 24. Consume be2tter 2012
  25. 25. DC4Cities 2013 2016 let existing and new data centres become energy adaptive
  26. 26. align workload to renewable energies availability
  27. 27. forecasts 1) 24h power budget 2) alt. working modes energy adaptive applications 3) selected mode DC4Cities gray box approach
  28. 28. Energy adaptive Batch scheduling How many parallel jobs, which ones to defer Trade replicas against latency Allocate resources against SLAs Energy adaptive WWW Energy adaptive IaaS Energy adaptive … …
  29. 29. ConclusionS
  30. 30. consume less, consume better, non-renewable power sources, DVFS, live-migration, deferrable workload, so many facets, non-deferrable workload, elasticity, VM packing, VOVO, VM balancing, white-box approach, black-box approach, solutions need to follow new capabilities and usage, priority over SLA, priority over savings, renewable power sources, fine grain power model, coarse grain power model, steady workload, bursty workload
  31. 31. 1.1 - 1.5 % in 2010 USA (Environmental Protection Agency, 2013)
  32. 32. .org

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