energy efficient resource management in virtualised datacenters
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Transcript of energy efficient resource management in virtualised datacenters
energy efficient resource management in virtualised datacenters
Fabien Hermenier
(Environmental Protection Agency, 2007)
of the 2005 budget1.5%
in 2010 ?3 %USA
1Consume less2003
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0 1 2 3 4VM #
Con
sum
ptio
n (W
att)
Node consumption statistics (Cluster edel, 128GB HDD)
the brown constant
110 W
175 W
2003“let’s turn off useless stuff”elnozahy et al. :
Packingconcentrate WWW
requests
2006 “look! look it’s moving”Hansen et al. :
live migrationN1 N2VM1
1) model power consumption 2) pack VMs 3) turn-off unused nodes 4) minimize(W) 5) profit
consume less, the theory
hw. heterogeneity env. capabilities performance vs. energy workload volatility data center size
,,consume less, the practice
BtrPlace proposalcore reconfiguration algorithm
users scripts
+ =specialized reconfiguration algorithm
spread(VM[2..3]); preserve({VM1},’ucpu’, 3); offline(@N4);
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)
The reconfiguration plan
BtrPlace
the core reconfiguration algorithm is modeled wrt. the impact of actions on resources
allDifferent(dhost
1 , d
host
2 ) ^d
host
1 = c
host
2 ! d
st
1 � c
ed
2 ^d
host
2 = c
host
1 ! d
st
2 � c
ed
1
spread({VM1,VM2}):
Premade variables, constraints
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
VM template migration duration migration payback time
hw. particularities
a fine-grain power model
workload particularities
energy-related variables are linked to core ones
energy-oriented constraintsMaxServerPower
DelayBtwMigrationsDelayBtwServerSwitch
PayBackTime
SpareCPUs
minEnergyCons
minGasEmissions
cap consumption
reduce ping-pong effects
migration as an investment
control the consolidation aggressiveness
optimisation criteria
500 1000 1500 2000 2500
1
234
5
6
7
Servers
Tim
e (m
inut
es)
P4G P4G + spare P4G + spare + vcpu P4G + spare + vcpu + delay
the core problem dominates scalability
coarse to fine grain optimisation
-16%
-47%
- 27%-7%
Consuming lessdealing with
THE BROWN CONSTANT
Consuming lessdealing with
UNAPPROPRIATE
HARDWARE
hw. community did not
chill
energy proportional servers, free cooling,
fanless processors
?workload agnostic
need to revamp software approaches migration
a balancing problem
2Consume better2012
2013 2016
let existing and new data centres become energy adaptive
DC4Cities
align workload to renewable energies availability
DC4Cities gray box approach
forecasts1) 24h power budget
2) alt. working modes
3) selected mode
energy adaptive applications
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 …
…
ConclusionS
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
in 20101.1 - 1.5 %
(Environmental Protection Agency, 2013)
USA
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