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Higher SLA Satisfaction in Datacenters 
with Continuous Placement Constraints 
Huynh Tu Dang 
hdang@polytech.unice.fr 
Fabien Hermenier 
fabien.hermenier@unice.fr
SLA for a virtualised application
SLA for a virtualised application 
spread the replicas
SLA for a virtualised application 
performance guarantee
SLA for a virtualised application 
low latency
reconfiguration algorithm 
N2 
N3 
time 
SLA: spread(VM1, VM2) 
VM2 
VM1 
N1
reconfiguration algorithm 
N2 
N3 
time 
SLA: spread(VM1, VM2) 
sys-admin query: offline(N1) 
VM2 
VM2 
VM1 
VM1 
N1 
Reconfiguration 
process
reconfiguration algorithm 
N2 
N3 
time 
SLA: spread(VM1, VM2) 
sys-admin query: offline(N1) 
VM2 
VM2 
VM1 
VM1 
N1 
with discrete restrictions
Discrete restriction is not enough 
not an unpredictable situation, 
an algorithmic issue
Evaluating the reliability of discrete 
placement constraints 
• simulate a 256-server datacenter 
• running 350 HA webapp (5,200 VMs) 
• BtrPlace as the reconfiguration algorithm 
• 4 reconfiguration scenarios that mimic industrial use case 
• 100 instances per scenario
Studied constraints 
spread 
among 
splitAmong 
maxOnline 
singleResource 
Capacity 
replicas on distinct servers for fault 
tolerance 
DBs on a same edge-switch for a 
fast synchronisation. 
webapp split over 2 clusters 
for disaster recovery 
240 nodes online at maximum 
to fit licensing policy 
keep resource for hypervisor 
management operations
scenario 
vertical elasticity 
Tiers 1 
Tiers 2 
Tiers 3
scenario 
vertical elasticity 
Tiers 1 
Tiers 2 
Tiers 3
scenariohorizontal elasticity 
Tiers 1 
Tiers 2 
Tiers 3
scenariohorizontal elasticity 
Tiers 1 
Tiers 2 
Tiers 3
scenarioboot storm x 400
scenarioserver failure
Migrations lead to unanticipated 
Scenario Violated 
SLAs 
placements 
Actions 
VM Boot Migrate Node Boot Node Shutdown 
Vertical Elasticity 40.72 0% 99.99% 0.005% 0.005% 
Horizontal Elasticity 0.19 99.82% 0.18% 2.82% 0% 
Server Failure 29.56 61.29% 35.89% 2.82% 0% 
Boot Storm 0.35 98.57% 1.43% 0% 0%
Migrationstend to violate relative 
100 
75 
50 
25 
0 
Vertical 
Elasticity 
placement constraints 
performance loss 
Horizontal 
Elasticity 
Server 
Failure 
Boot 
Storm 
Violations 
spread among splitAmong maxOnline 
spof 
failure
Trading unreliable 
discrete constraints … 
spread(VM[1,2]) 
we addressed an 
assignment problem
… for safe 
continuous constraints 
spread(VM[1,2]) 
we must address 
a scheduling problem
Continuous placement constraints 
with
from discrete to continuous 
among|simpleAmong 
N1 
N2 N3 
N4 N5 
N6 
stay on a same partition by the end of 
the reconfiguration process
from discrete to continuous 
among|simpleAmong 
N1 
N2 N3 
N4 N5 
N6 
stay on a same partition by the end of 
the reconfiguration process
from discrete to continuous 
among|simpleAmong 
N1 
N2 N3 
N4 N5 
N6 
stay on a same partition by the end of 
the reconfiguration process
from discrete to continuous 
among|simpleAmong 
N1 
N2 N3 
N4 N5 
Disallow movements between partitions 
• basic knowledge of a reconfiguration process 
• still an assignment problem 
N6
from discrete to continuous 
among|simpleAmong 
N1 
N2 N3 
N4 N5 
Disallow movements between partitions 
• basic knowledge of a reconfiguration process 
• still an assignment problem 
N6
continuous spread 
discrete spread(VM[1,2]) ::= continuous spread(VM[1,2]) ::= 
allDi↵erent(dhost 
1 , dhost 
2 ) 
allDi↵erent(dhost 
1 , dhost 
2 ) ^ 
dhost 
1 = chost 
2 =) astart 
1 # aend 
2 ^ 
dhost 
2 = chost 
1 =) astart 
2 # aend 
1 
Disallow temporary overlapping 
• require to know this may happen 
• scheduling 101
continuous maxOnline 
discrete maxOnline(N[1..10], 7)::= 
continuous maxOnline(N[1..10], 7)::= 
X10 
i=1 
nqi 
 7 
8i 2 [1, 10], non 
i = 
⇢ 
0 if nq 
i = 1 
astart 
i otherwise 
noff 
i = 
⇢ 
max(T) if nq 
i = 0 
aend 
i otherwise 
i # t ^ noff 
8t 2 T, card({i|non 
i })  7 
scheduling 201 
detailed knowledge of a 
reconfiguration process 
harder to imagine, 
model & implement
Performance overhead 
5 10 20 50 100 
Duration (sec.) 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 
10 20 50 100 
Duration (sec.) 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 
0 20 40 60 80 100vertical elasticity 
discrete 
continuous 
5 10 20 50 100 200 
Duration (sec.) 
5 10 20 50 100 
Duration (sec.) 
Solved instances 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 boot storm 
horitzontal elasticity server failure
Performance overhead 
5 10 20 50 100 
Duration (sec.) 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 
10 20 50 100 
Duration (sec.) 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 
0 20 40 60 80 100vertical elasticity 
discrete 
continuous 
5 10 20 50 100 200 
Duration (sec.) 
5 10 20 50 100 
Duration (sec.) 
Solved instances 
Solved instances 
discrete 
continuous 
0 20 40 60 80 100 boot storm 
horitzontal elasticity server failure
Conclusions 
• discrete restriction is not enough 
• continuous restriction is a solution 
• a different view on the problem 
• challenging, but still possible to implement
Future Work 
• a broader range of constraints and objectives 
• reducing performance overhead 
• static analysis to detect un-necessary 
continuous constraints 
• controlled relaxation to handle hard situations
http://btrp.inria.fr 
open source, 20+ placement constraints, 
demo, tutorials, everything for reproducibility

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Higher SLA Satisfaction in Datacenters with Continuous VM Placement Constraints

  • 1. Higher SLA Satisfaction in Datacenters with Continuous Placement Constraints Huynh Tu Dang hdang@polytech.unice.fr Fabien Hermenier fabien.hermenier@unice.fr
  • 2. SLA for a virtualised application
  • 3. SLA for a virtualised application spread the replicas
  • 4. SLA for a virtualised application performance guarantee
  • 5. SLA for a virtualised application low latency
  • 6. reconfiguration algorithm N2 N3 time SLA: spread(VM1, VM2) VM2 VM1 N1
  • 7. reconfiguration algorithm N2 N3 time SLA: spread(VM1, VM2) sys-admin query: offline(N1) VM2 VM2 VM1 VM1 N1 Reconfiguration process
  • 8. reconfiguration algorithm N2 N3 time SLA: spread(VM1, VM2) sys-admin query: offline(N1) VM2 VM2 VM1 VM1 N1 with discrete restrictions
  • 9. Discrete restriction is not enough not an unpredictable situation, an algorithmic issue
  • 10. Evaluating the reliability of discrete placement constraints • simulate a 256-server datacenter • running 350 HA webapp (5,200 VMs) • BtrPlace as the reconfiguration algorithm • 4 reconfiguration scenarios that mimic industrial use case • 100 instances per scenario
  • 11. Studied constraints spread among splitAmong maxOnline singleResource Capacity replicas on distinct servers for fault tolerance DBs on a same edge-switch for a fast synchronisation. webapp split over 2 clusters for disaster recovery 240 nodes online at maximum to fit licensing policy keep resource for hypervisor management operations
  • 12. scenario vertical elasticity Tiers 1 Tiers 2 Tiers 3
  • 13. scenario vertical elasticity Tiers 1 Tiers 2 Tiers 3
  • 18. Migrations lead to unanticipated Scenario Violated SLAs placements Actions VM Boot Migrate Node Boot Node Shutdown Vertical Elasticity 40.72 0% 99.99% 0.005% 0.005% Horizontal Elasticity 0.19 99.82% 0.18% 2.82% 0% Server Failure 29.56 61.29% 35.89% 2.82% 0% Boot Storm 0.35 98.57% 1.43% 0% 0%
  • 19. Migrationstend to violate relative 100 75 50 25 0 Vertical Elasticity placement constraints performance loss Horizontal Elasticity Server Failure Boot Storm Violations spread among splitAmong maxOnline spof failure
  • 20. Trading unreliable discrete constraints … spread(VM[1,2]) we addressed an assignment problem
  • 21. … for safe continuous constraints spread(VM[1,2]) we must address a scheduling problem
  • 23. from discrete to continuous among|simpleAmong N1 N2 N3 N4 N5 N6 stay on a same partition by the end of the reconfiguration process
  • 24. from discrete to continuous among|simpleAmong N1 N2 N3 N4 N5 N6 stay on a same partition by the end of the reconfiguration process
  • 25. from discrete to continuous among|simpleAmong N1 N2 N3 N4 N5 N6 stay on a same partition by the end of the reconfiguration process
  • 26. from discrete to continuous among|simpleAmong N1 N2 N3 N4 N5 Disallow movements between partitions • basic knowledge of a reconfiguration process • still an assignment problem N6
  • 27. from discrete to continuous among|simpleAmong N1 N2 N3 N4 N5 Disallow movements between partitions • basic knowledge of a reconfiguration process • still an assignment problem N6
  • 28. continuous spread discrete spread(VM[1,2]) ::= continuous spread(VM[1,2]) ::= allDi↵erent(dhost 1 , dhost 2 ) allDi↵erent(dhost 1 , dhost 2 ) ^ dhost 1 = chost 2 =) astart 1 # aend 2 ^ dhost 2 = chost 1 =) astart 2 # aend 1 Disallow temporary overlapping • require to know this may happen • scheduling 101
  • 29. continuous maxOnline discrete maxOnline(N[1..10], 7)::= continuous maxOnline(N[1..10], 7)::= X10 i=1 nqi  7 8i 2 [1, 10], non i = ⇢ 0 if nq i = 1 astart i otherwise noff i = ⇢ max(T) if nq i = 0 aend i otherwise i # t ^ noff 8t 2 T, card({i|non i })  7 scheduling 201 detailed knowledge of a reconfiguration process harder to imagine, model & implement
  • 30. Performance overhead 5 10 20 50 100 Duration (sec.) Solved instances discrete continuous 0 20 40 60 80 100 10 20 50 100 Duration (sec.) Solved instances discrete continuous 0 20 40 60 80 100 0 20 40 60 80 100vertical elasticity discrete continuous 5 10 20 50 100 200 Duration (sec.) 5 10 20 50 100 Duration (sec.) Solved instances Solved instances discrete continuous 0 20 40 60 80 100 boot storm horitzontal elasticity server failure
  • 31. Performance overhead 5 10 20 50 100 Duration (sec.) Solved instances discrete continuous 0 20 40 60 80 100 10 20 50 100 Duration (sec.) Solved instances discrete continuous 0 20 40 60 80 100 0 20 40 60 80 100vertical elasticity discrete continuous 5 10 20 50 100 200 Duration (sec.) 5 10 20 50 100 Duration (sec.) Solved instances Solved instances discrete continuous 0 20 40 60 80 100 boot storm horitzontal elasticity server failure
  • 32. Conclusions • discrete restriction is not enough • continuous restriction is a solution • a different view on the problem • challenging, but still possible to implement
  • 33. Future Work • a broader range of constraints and objectives • reducing performance overhead • static analysis to detect un-necessary continuous constraints • controlled relaxation to handle hard situations
  • 34. http://btrp.inria.fr open source, 20+ placement constraints, demo, tutorials, everything for reproducibility