energy efficient resource management in virtualised datacenters
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The document discusses strategies for energy-efficient resource management in virtualized data centers, highlighting methods such as VM packing, turning off unused nodes, and optimizing power consumption through algorithms like btrplace. It emphasizes the need for fine-grain power models and adaptive applications to align workloads with renewable energy availability. The conclusions stress a holistic approach to energy consumption that prioritizes both efficiency and sustainability in data management practices.
![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](https://image.slidesharecdn.com/wilwyzvstsfdl9lrckqg-signature-25c372b8bfa01e2f99ecf6921938634dea6c310d700f52e90ea963c68c504874-poli-141016091440-conversion-gate01/85/energy-efficient-resource-management-in-virtualised-datacenters-10-320.jpg)
energy efficient resource management in virtualised datacenters
- 1. energy efficient resource management in virtualised datacenters Fabien Hermenier
- 2. USA 3 % of the 2005 budget (Environmental Protection Agency, 2007) 1.5% in 2010 ?
- 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. 2003elnozahy et al. : “let’s turn off useless stuff” Packing concentrate WWW requests
- 6. 2006 Hansen et al. : “look! look it’s moving” live migration N1 VM1 N2
- 7. consume less, the theory 1) model power consumption 2) pack VMs 3) turn-off unused nodes 4) minimize(W) 5) profit
- 8. consume less, the practice hw. heterogeneity env. capabilities performance vs. energy workload volatility data center size , ,
- 9. BtrPlace proposal core reconfiguration algorithm users scripts + = specialized reconfiguration algorithm
- 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. the core reconfiguration algorithm is modeled wrt. the impact of actions on resources
- 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. Constraint Programming ! ! ! to the rescue
- 14. 2010 2013 Energy aware ICT optimization policies (+ btrPlace)
- 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. energy-related variables are linked to core ones
- 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. 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. coarse to fine grain optimisation -16% -47% - 27% -7%
- 20. Consuming less dealing with THE BROWN CONSTANT
- 21. Consuming less dealing with UNAPPROPRIATE HARDWARE
- 22. energy proportional servers, hw. community did not chill free cooling, fanless processors
- 23. ? workload agnostic need to revamp software approaches migration a balancing problem
- 25. DC4Cities 2013 2016 let existing and new data centres become energy adaptive
- 26. align workload to renewable energies availability
- 27. forecasts 1) 24h power budget 2) alt. working modes energy adaptive applications 3) selected mode DC4Cities gray box approach
- 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. ConclusionS
- 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. 1.1 - 1.5 % in 2010 USA (Environmental Protection Agency, 2013)
- 32. .org