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Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration

Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration. Makhlouf Hadji Ingénieur de Recherche m akhlouf.hadji@irt-systemx.fr. FONDATION DE COOPERATION SCIENTIFIQUE. NOS VALEURS. E xcellence Talents Résultats Echanges. S ouplesse Fonctionnement Partenariat

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Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration

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  1. Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration Makhlouf Hadji Ingénieur de Recherche makhlouf.hadji@irt-systemx.fr

  2. FONDATION DE COOPERATION SCIENTIFIQUE

  3. NOS VALEURS • Excellence • Talents • Résultats • Echanges • Souplesse • Fonctionnement • Partenariat • Projets • Rigueur • Propriété Intellectuelle • Confidentialité • Exécution

  4. PROGRAMMES DE R&D Ingénierie numérique des Systèmes et Composants

  5. 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

  6. I- Smart Placement in Clouds

  7. 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.

  8. Smart Placement in Clouds • French Providers Point of View

  9. 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

  10. 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.

  11. 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)

  12. 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)

  13. 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).

  14. 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.

  15. II- Live Migration of VMs

  16. Live Migration of VMs • Migration process: ESX Xen Hyper-V KVM OFF

  17. Live Migration of VMs PolyhedralApproachs Polytops, faces and facets Subject To constraints: facets face

  18. 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…

  19. Live Migration of VMs PolyhedralApproachs

  20. Live Migration of VMs PolyhedralApproachs Number of used servers whentakingintoaccount Migrations

  21. Convergence Time (in seconds) of Migration Algorithm:

  22. Live Migration of VMs PolyhedralApproachs Percentage of GainedEnergywhen Migration isUsed

  23. Thankyou

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