國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Optimal Provisioning for Elastic Service Oriented Virtual Network Request in Cloud Computing 劉冠逸

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Outline Introduction Problem description Genetic Algorithm-based Heuristic Algorithm (GAH) Simulations

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Introduction Cloud computing paradigm enables users to access services and applications hosted in data centers based on their requirements. The service or application request submitted to a data center can be abstracted as a virtual network (VN) request, which consists of a set of VN nodes and VN edges.

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Virtual Network

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Introduction How to efficiently provision VN 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.

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Problem description (I)

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Problem description (II)

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Problem description (III) The revenue R(GV) generated by provisioning a VN request can be calculated as follows:

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Greedy VN Provisioning Algorithm(GVNP) sss

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Greedy VN Provisioning Algorithm(GVNP) sss

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Genetic Algorithm-based Heuristic Algorithm (GAH) Chromosome Coding Chromosome Operations Genetic Algorithm-based Heuristic Algorithm (GAH)

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory 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

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Chromosome Operations Cloning Crossover Mutation Feasibility checking Selection

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Chromosome Operations Cloning The cloning operation involves generating theinitial population The GA procedure begins its iterations from this population

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Chromosome Operations Crossover

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory 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 p m (from to 0.1)

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Chromosome Operations Feasibility checking Some of the newly generated children may not be feasible solutions for the original problem.

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory 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

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Genetic Algorithm-based Heuristic Algorithm (GAH)

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Simulations Use the ITALYNET (Figure 4) with 20 nodes and 36 links as substrate network in our simulation

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Simulations

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory Simulations

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory 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

國立清華大學高速通訊與計算實驗室 NTHU High-Speed Communication & Computing Laboratory The End