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Optimal Provisioning for Elastic Service Oriented Virtual Network Request in Cloud Computing

Optimal Provisioning for Elastic Service Oriented Virtual Network Request in Cloud Computing. 101062558 劉冠逸. Outline. Introduction Problem description G enetic A lgorithm-based H euristic Algorithm (GAH) Simulations. Introduction.

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Optimal Provisioning for Elastic Service Oriented Virtual Network Request in Cloud Computing

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  1. Optimal Provisioning for Elastic Service Oriented Virtual Network Request in Cloud Computing 101062558 劉冠逸

  2. Outline Introduction Problem description Genetic Algorithm-based Heuristic Algorithm (GAH) Simulations

  3. Introduction Cloud computing paradigm enables users to accessservices and applications hosted in data centers based ontheir requirements. The service or application request submitted to a datacenter can be abstracted as a virtual network (VN) request,which consists of a set of VN nodes and VN edges.

  4. Virtual Network

  5. Introduction How to efficiently provisionVN requests in multi datacenters is of utmost importance For the elastic resource requirement services, providers need to make sure the QoS or SLAs are satisfied.

  6. Problem description (I)

  7. Problem description (II) For a provisioned VN request , we define the gross income GI() as: The cost C() of provisioning a VN request :

  8. Problem description (III) The revenue R(GV) generated by provisioning a VN request can be calculated as follows:

  9. Greedy VN Provisioning Algorithm(GVNP) sss

  10. Greedy VN Provisioning Algorithm(GVNP) sss

  11. Genetic Algorithm-based Heuristic Algorithm (GAH) Chromosome Coding Chromosome Operations Genetic Algorithm-based Heuristic Algorithm (GAH)

  12. Chromosome Coding The number of columns in the array equals to the number of server nodes in substrate network The total number of element “1” in the array equals to the number of VN nodes in a VN request

  13. Chromosome Operations Cloning Crossover Mutation Feasibility checking Selection

  14. Chromosome Operations • Cloning • The cloning operation involves generating theinitial population • The GA procedure begins its iterations from this population

  15. Chromosome Operations Crossover

  16. Chromosome Operations • Mutation • The mutation operation is used to prevent solutions from being trapped at a local optimum • Mutation is done in the children population, by changing the values of some genes with a small probability pm (from 0.001 to 0.1)

  17. Chromosome Operations • Feasibility checking • Some of the newly generated children may not be feasible solutions for the original problem.

  18. Chromosome Operations • Selection • The chromosome selection is to select parent chromosomes from the particular generation of population, and assign reproductive opportunities to these selected chromosomes

  19. Genetic Algorithm-based Heuristic Algorithm (GAH)

  20. Simulations Use the ITALYNET (Figure 4) with 20 nodes and 36 links as substrate network in our simulation

  21. Simulations

  22. Simulations

  23. Conclusion In this work we address the problem of optimal provisioning for elastic service oriented VN request in cloud-based datacenters. We model this problem as a mathematical optimization problem by using mixed integer programming and propose a genetic algorithm based heuristic algorithm for solving this NP-hard problem efficiently. The experimental results demonstrate that the solution obtained by our approach is near to the optimal solution

  24. The End

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