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Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration. Makhlouf Hadji Ingénieur de Recherche m [email protected] FONDATION DE COOPERATION SCIENTIFIQUE. NOS VALEURS. E xcellence Talents Résultats Echanges. S ouplesse Fonctionnement Partenariat

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slide1

Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration

Makhlouf Hadji

Ingénieur de Recherche

[email protected]

nos valeurs
NOS VALEURS
  • Excellence
    • Talents
    • Résultats
    • Echanges
  • Souplesse
    • Fonctionnement
    • Partenariat
    • Projets
  • Rigueur
    • Propriété Intellectuelle
    • Confidentialité
    • Exécution
programmes de r d
PROGRAMMES DE R&D

Ingénierie numérique des Systèmes et Composants

projets r d
PROJETS R&D

Projets opérationnels depuis le lancement

29

ETP/an sur 3 ans

Partenaires Industriels

Partenaires Académiques

Thèses

110

42

12

Projets Opérationnels

M€ Financement Industriel

9

15,5

smart placement in clouds

Infrastructure servers

Smart Placement in Clouds

VM Placement problem

Problem:Based on allocating and hostingN VMson a physical infrastructure of X Serveurs, whatis the best manner to optimally place workloads to minimizedifferent infrastructure costs ?

Non optimal

placement

ESX 1

ESX 2…

ESX N

VMsdemand management

Cloud End-Users

Optimal placement

ESX 1

ESX 2…

ESX N

VMs placement strategy

???

Benefits

  • Resourcesoptimization,
  • Minimization of infrastructure costs,
  • Energyconsumptionoptimization.

ESX 1

ESX 2…

ESX N

Define the best strategy to place VMsworkloadsleading to optimallyreduce infrastructure costs.

Challenges of the problem:

  • Exponential number of cases to enumerate.
smart placement in clouds1
Smart Placement in Clouds
  • French Providers Point of View
smart placement in clouds2

Infrastructure serveurs

Smart Placement in Clouds

Due to fluctuations in users’ demands, we use Auto-Regressive (AR(k)) process, to handlewith future demands:

Gestion de la demande de VM

Utilisateurs des services Cloud

Forcasting & Scheduling

large

medium

small

small

ProblemComplexity :

NP-Hard Problem: One canconstructeasily a plynomialreductionfrom the NP-Hard notaryproblem of the Bin-Packing.

ESX 1

ESX 2…

ESX N

smart placement in clouds3
Smart Placement in Clouds

Mathematical formulation:

Formulation as ILP:

The correspondingmathematical model is an IntegerLinearProgramming: difficulties to characterize the convexhull of the consideredproblem and the optimal solution.

smart placement in clouds4
Smart Placement in Clouds

Minimum Cost Maximum Flow Algorithm

Instance i

(1; 0)

(1; 0,33)

(2; 0)

(2; 0,23)

(1; 0)

(Di; 0)

(Di; 0)

(1; 0,14)

S

T

Legend: (capacity; cost)

smart placement in clouds5
Smart Placement in Clouds

Minimum CostMaximum Flow Algorithm

Small Instance

(1; 0)

(1; 0,33)

(2; 0)

(2; 0,23)

(1; 0)

(1; 0,14)

(D1; 0)

(D1; 0)

Medium Instance

(0; ∞)

(0; 0)

S

T

(D2; 0)

(D2; 0)

(2; 0)

(2; 0,23)

(1; 0)

(1; 0,14)

smart placement in clouds6
Smart Placement in Clouds

Simulation Tests: Case of (0;1) RandomCosts

RandomHostingCosts Scenario

Weconsider (0; 1) Randomhostingcostsbetweeneach couple of vertices (a, b), where a is a fictif node, and b is a physical machine (server).

smart placement in clouds7
Smart Placement in Clouds

Simulations Tests: Case of Inverse HostingCosts:

Inverse HostingCosts Scenario

Weconsiderinversedhostingcostsfunctionbetweeneach couple of vertices (a, b), where a is a fictif node, and b is a physical machine:

Whererepresents the availablecapacity on the considered arc. est une fonction non nulle.

live migration of vms
Live Migration of VMs
  • Migration process:

ESX

Xen

Hyper-V

KVM

OFF

slide17

Live Migration of VMs

PolyhedralApproachs

Polytops, faces and facets

Subject To constraints:

facets

face

live migration of vms1
Live Migration of VMs

PolyhedralApproachs

SomeValidInequalities of ourProblem:

  • Decision Variables:

if A VM k ismigratedfrom i to j (0 else).

  • Preventbackword migration of a VM:
  • Server’s destination uniqueness of a VM migration:
  • Servers’ power consumption limitation constraints:
  • Etc…
live migration of vms2
Live Migration of VMs

PolyhedralApproachs

live migration of vms3
Live Migration of VMs

PolyhedralApproachs

Number of used servers whentakingintoaccount Migrations

live migration of vms4
Live Migration of VMs

PolyhedralApproachs

Percentage of GainedEnergywhen Migration isUsed

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