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BIN REPACKING SCHEDULING 
IN 
VIRTUALIZED DATACENTERS 
Fabien HERMENIER, FLUX, U. Utah & OASIS, INRIA/CNRS, U. Nice 
Sophi...
CONTEXT 
datacenters and virtualization 
2
DATACENTERS 
interconnected servers 
hosting 
distributed applications 
3
RESOURCES 
server 
capacities 
application 
requirements 
4
VIRTUALIZATION 
applications embedded in 
Virtual Machines 
colocated on any server 
manipulable 
1 datacenter 
4 servers,...
ACTION 
• stop/suspend 
• launch/resume 
• migrate live 
has a known duration 
consumes resources 
impacts VM performance ...
ACTION 
live migration 
has a known duration 
consumes resources 
impacts VM performance 
6
DYNAMIC SYSTEM 
Applications Servers 
• submission 
• removal (complete, crash) 
• requirement change (load 
spikes) 
• ad...
DYNAMIC 
RECONFIGURATION 
8
RECONFIGURATION PLAN 
migrate S1 - S2 migrate S4 - S1 launch S4 
time 
t=0 
9
RECONFIGURATION 
PROBLEM 
given an initial configuration and new requirements: 
• find a new viable configuration 
• assoc...
+ USER REQUIREMENTS 
Clients Administrators 
• fault-tolerance w. replication 
• performance w. isolation 
• resource matc...
ENTROPY 
an autonomous VM manager 
12
PLAN MODULE 
• reconfiguration problem: vector packing + cumulative 
scheduling + side constraints (NP-hard) 
Constraint 
...
ABSTRACTION 
•1VM = 0 or 1 action 
•min Σ end(A) 
actions A 
•full resource requirements 
at transition 
14
Compound CP model 
Packing + Scheduling 
decomposition degrades the objective and may result 
in a feasible packing withou...
Producer/Consumer 
1 action = 2 tasks / no-wait 
home-made cumulatives in every dimension 
16
SIDE CONSTRAINTS 
ban unary 
fence unary 
spread alldifferent + precedences 
lonely disjoint 
capacity gcc 
among element ...
REPAIR 
candidate VMs 
fixed 
heuristically 
resource+placement constraints come with a new service: 
return a feasible su...
EVALUATION 
19
PROTOCOL 
500 - 2,500 homogeneous servers 
2,000 - 10,000 heterogeneous VMs 
extra-large/high-memory EC2 instances 
3-tier...
LOAD 
solving time 1,000 servers 
70% = standard average consolidation ratio 
21
SCALABILITY 
solving time 5 VMs : 1 server 
2,000 servers = standard capacity of a container 
22
field to investigate for packing+scheduling 
≠ usages to optimize: CPU, energy, revenue 
side constraints important for us...
user constraint catalog 
routing and application topology constraints 
soft/explained user constraints 
new results availa...
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Bin repacking scheduling in virtualized datacenters

Fabien Hermenier, Sophie Demassey, and Xavier Lorca.
In Proceedings of the 17th international conference on Principles and practice of constraint programming (CP'11). Springer-Verlag, Berlin, Heidelberg, pages 27-41.

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Bin repacking scheduling in virtualized datacenters

  1. 1. BIN REPACKING SCHEDULING IN VIRTUALIZED DATACENTERS Fabien HERMENIER, FLUX, U. Utah & OASIS, INRIA/CNRS, U. Nice Sophie DEMASSEY, TASC, INRIA/CNRS, Mines Nantes Xavier LORCA, TASC, INRIA/CNRS, Mines Nantes 1
  2. 2. CONTEXT datacenters and virtualization 2
  3. 3. DATACENTERS interconnected servers hosting distributed applications 3
  4. 4. RESOURCES server capacities application requirements 4
  5. 5. VIRTUALIZATION applications embedded in Virtual Machines colocated on any server manipulable 1 datacenter 4 servers, 3 apps 5
  6. 6. ACTION • stop/suspend • launch/resume • migrate live has a known duration consumes resources impacts VM performance 6
  7. 7. ACTION live migration has a known duration consumes resources impacts VM performance 6
  8. 8. DYNAMIC SYSTEM Applications Servers • submission • removal (complete, crash) • requirement change (load spikes) • addition • removal (power off, crash) • availability change 7
  9. 9. DYNAMIC RECONFIGURATION 8
  10. 10. RECONFIGURATION PLAN migrate S1 - S2 migrate S4 - S1 launch S4 time t=0 9
  11. 11. RECONFIGURATION PROBLEM given an initial configuration and new requirements: • find a new viable configuration • associate actions to VMs • schedule the actions to resolve violations and dependencies s.t every action is complete as early as possible 10
  12. 12. + USER REQUIREMENTS Clients Administrators • fault-tolerance w. replication • performance w. isolation • resource matchmaking • maintenance • security w. isolation • shared resource control to satisfy at any time during the reconfiguration 11
  13. 13. ENTROPY an autonomous VM manager 12
  14. 14. PLAN MODULE • reconfiguration problem: vector packing + cumulative scheduling + side constraints (NP-hard) Constraint Programming • trade-off fast+good • composable online • easily adaptable offline 13
  15. 15. ABSTRACTION •1VM = 0 or 1 action •min Σ end(A) actions A •full resource requirements at transition 14
  16. 16. Compound CP model Packing + Scheduling decomposition degrades the objective and may result in a feasible packing without reconfiguration plan shared constraints resource+side separated branching heuristic 1-packing 2-scheduling 15
  17. 17. Producer/Consumer 1 action = 2 tasks / no-wait home-made cumulatives in every dimension 16
  18. 18. SIDE CONSTRAINTS ban unary fence unary spread alldifferent + precedences lonely disjoint capacity gcc among element gather allequals mostly spread nvalue 17
  19. 19. REPAIR candidate VMs fixed heuristically resource+placement constraints come with a new service: return a feasible sub-configuration 18
  20. 20. EVALUATION 19
  21. 21. PROTOCOL 500 - 2,500 homogeneous servers 2,000 - 10,000 heterogeneous VMs extra-large/high-memory EC2 instances 3-tiers HA web applications with replica 50% VM grow 30% uCPU 4% VM launch, 2% VM stop 1% server stop 20
  22. 22. LOAD solving time 1,000 servers 70% = standard average consolidation ratio 21
  23. 23. SCALABILITY solving time 5 VMs : 1 server 2,000 servers = standard capacity of a container 22
  24. 24. field to investigate for packing+scheduling ≠ usages to optimize: CPU, energy, revenue side constraints important for users Entropy: CP-based resource manager, fast, scalable, generic, composable, flexible, easy to maintain http://entropy.gforge.inria.fr/ CONCLUSION 23
  25. 25. user constraint catalog routing and application topology constraints soft/explained user constraints new results available: http://sites.google.com/site/hermenierfabien/ PERSPECTIVES 24

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