Dynamic Resource Allocation in Clouds: Smart Placement with Live
MigrationMakhlouf Hadji
Ingénieur de [email protected]
FONDATION DE COOPERATION SCIENTIFIQUE
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NOS VALEURS
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PROGRAMMES DE R&D
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Ingénierie numérique des Systèmes et Composants
PROJETS R&D Projets opérationnels depuis le lancement
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Projets Opérationnels
M€ Financement Industriel
915,5
ETP/an sur 3 ans
Partenaires Industriels
Partenaires Académiques
1104212
Thèses29
I- Smart Placement in Clouds
Smart Placement in Clouds
7
Define the best strategy to place VMs workloads leading to optimally reduce infrastructure costs.
ESX 1 ESX 2… ESX N
Infrastructure servers
???
Cloud End-UsersVMs demand management
VMs placement strategy
Problem: Based on allocating and hosting N VMs on a physical infrastructure of X Serveurs, what is the best manner to optimally place workloads to minimize different infrastructure costs ?
Benefits Resources optimization, Minimization of infrastructure costs, Energy consumption optimization.
VM Placement problem
ESX 1 ESX 2… ESX N
Non optimalplacement
ESX 1 ESX 2… ESX N
Optimal placement
Challenges of the problem: Exponential number of cases to enumerate.
8
Smart Placement in Clouds
French Providers Point of View
9
Smart Placement in Clouds
ESX 1 ESX 2… ESX N
Infrastructure serveurs
Utilisateurs des services Cloud
Gestion de la demande de VM
Forcasting & Scheduling
small
small
mediumlarge
Due to fluctuations in users’ demands, we use Auto-Regressive (AR(k)) process, to handle with future demands:
Problem Complexity :NP-Hard Problem: One can construct easily a plynomial reduction from the NP-Hard notary problem of the Bin-Packing.
tit
k
iit dd
1
10
Smart Placement in CloudsMathematical formulation:
Formulation as ILP:The corresponding mathematical model is an Integer Linear Programming: difficulties to characterize the convex hull of the considered problem and the optimal solution.
else. 0
iserver in hosted is VM if 1
,,
,
,1,,
:ToSubject
min
1
1 1 1 1
jij
ij
j
N
iij
ijijij
N
i
I
j
N
i
I
jijjijij
y
jiNx
Ijdx
NiIjyCx
xPyZ
11
Smart Placement in CloudsMinimum Cost Maximum Flow Algorithm
S T
(1; 0,33)
(2; 0,23)
(1; 0,14)
(1; 0)
(2; 0)
(1; 0)
(Di; 0
)(Di; 0)
Instance i
Legend: (capacity; cost)
12
Smart Placement in CloudsMinimum Cost Maximum Flow Algorithm
S T
(1; 0,33)
(2; 0,23)
(1; 0,14)
(1; 0)
(2; 0)
(1; 0)
(D1; 0
)(D1; 0)
Small Instance
(0; ∞)
(2; 0,23)
(1; 0,14)
(0; 0)
(2; 0)
(1; 0)
Medium Instance
(D2; 0) (D2; 0)
13
Smart Placement in CloudsSimulation Tests: Case of (0;1) Random Costs
We consider (0; 1) Random hosting costs between each couple of vertices (a, b), where a is a fictif node, and b is a physical machine (server).
Random Hosting Costs Scenario
14
Smart Placement in CloudsSimulations Tests: Case of Inverse Hosting Costs:
Inverse Hosting Costs Scenario
We consider inversed hosting costs function between each couple of vertices (a, b), where a is a fictif node, and b is a physical machine:
Where represents the available capacity on the considered arc. est une fonction non nulle.
ababab
ab gCCf
g otherwise ,0 if )(
1
abC f
II- Live Migration of VMs
Live Migration of VMs
Migration process:
16
ESXXen
KVM Hyper-V O
FF
Polytops, faces and facets
facetsface
jjxcMin
m1ibxa ijij ,...,,Subject To constraints:
nixi ,...,1,1,0
*x
Live Migration of VMs
Polyhedral Approachs
11 x
22 x
20
18
Live Migration of VMs
Decision Variables: if A VM k is migrated from i to j (0 else). Prevent backword migration of a VM:
Server’s destination uniqueness of a VM migration:
Servers’ power consumption limitation constraints:
Etc…
Polyhedral Approachs
Some Valid Inequalities of our Problem:
1ijkZ
1' jlkijk ZZ
1'
,1
m
ijjijkZ
jcurrentjjijk
m
ik
qi
k
yppzp
1,max,
'
1 1
19
Live Migration of VMsPolyhedral Approachs
otherwise 0idle is server if 1
otherwise 0j, toi from migrated is VM if 1
'
1
1
',,',1,,1',,1,',,,1,1
:ToSubject
'max
0
'
1 max,
'
1,
'
1 1
'
1 1,max,
'
,1
'
'
1
'
1
'
1 1,
iy
z
Ttz
P
Pmy
yqz
yPPzp
z
kkjlmlqkqkmijmizz
zpyPM
i
kijk
kijk
m
i j
m
jcurrentj
i
m
jii
q
kijk
m
i
q
kjcurrentjjijkk
m
ijjijk
jijlkijk
m
i
m
i
m
j
q
kijkkiidli
i
i
i
20
Live Migration of VMsPolyhedral Approachs
Number of used servers when taking into account Migrations
21
Convergence Time (in seconds) of Migration Algorithm:
22
Live Migration of VMsPolyhedral Approachs
Percentage of Gained Energy when Migration is Used
5 10 15 20 25 30
10 35,55 36,59 00 00 00 00
20 27,29 34,00 35,23 38,50 00 00
30 17,48 27,39 35,21 40,32 41,89 36,65
40 16,77 18,85 22,02 32,31 39,90 40,50
50 10,86 16,17 19,85 22,30 39,20 36,52
60 08,63 14,29 18,01 22,13 25,15 30,68
70 08,10 14,00 14,86 15,90 22,91 23,20
80 07,01 10,20 10,91 15,34 17,02 21,60
90 06,80 09,32 10,31 14,70 16,97 19,20
100 05,90 07,50 08,40 12,90 16,00 14,97
Thank you
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