The document discusses bin repacking scheduling in virtualized datacenters, highlighting the dynamic nature of server applications and the need for reconfiguration to meet resource requirements. It presents a method for managing virtual machines through a constraint programming model that addresses the scheduling of actions to maintain performance while minimizing resource consumption. Key features include scalability and adaptability of the system to different configurations and user constraints.

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
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2. CONTEXT
datacenters and virtualization
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3. DATACENTERS
interconnected servers
hosting
distributed applications
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4. RESOURCES
server
capacities
application
requirements
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5. VIRTUALIZATION
applications embedded in
Virtual Machines
colocated on any server
manipulable
1 datacenter
4 servers, 3 apps 5

6. ACTION
• stop/suspend
• launch/resume
• migrate live
has a known duration
consumes resources
impacts VM performance
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7. ACTION
live migration
has a known duration
consumes resources
impacts VM performance
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8. DYNAMIC SYSTEM
Applications Servers
• submission
• removal (complete, crash)
• requirement change (load
spikes)
• addition
• removal (power off, crash)
• availability change
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9. DYNAMIC
RECONFIGURATION
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10. RECONFIGURATION PLAN
migrate S1 - S2 migrate S4 - S1 launch S4
time
t=0
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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
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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
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13. ENTROPY
an autonomous VM manager
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14. PLAN MODULE
• reconfiguration problem: vector packing + cumulative
scheduling + side constraints (NP-hard)
Constraint
Programming
• trade-off fast+good
• composable online
• easily adaptable offline
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15. ABSTRACTION
•1VM = 0 or 1 action
•min Σ end(A)
actions A
•full resource requirements
at transition
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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
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17. Producer/Consumer
1 action = 2 tasks / no-wait
home-made cumulatives in every dimension
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18. SIDE CONSTRAINTS
ban unary
fence unary
spread alldifferent + precedences
lonely disjoint
capacity gcc
among element
gather allequals
mostly spread nvalue
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19. REPAIR
candidate VMs
fixed
heuristically
resource+placement constraints come with a new service:
return a feasible sub-configuration
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20. EVALUATION
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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
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22. LOAD
solving time 1,000 servers
70% = standard average consolidation ratio
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23. SCALABILITY
solving time 5 VMs : 1 server
2,000 servers = standard capacity of a container
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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
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25. user constraint catalog
routing and application topology constraints
soft/explained user constraints
new results available:
http://sites.google.com/site/hermenierfabien/
PERSPECTIVES
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