optimal provisioning for elastic service oriented virtual network request in cloud computing
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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. - PowerPoint PPT PresentationTRANSCRIPT
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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
101062558 劉冠逸
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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
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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.
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國立清華大學高速通訊與計算實驗室NTHU High-Speed Communication & Computing Laboratory
Virtual Network
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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.
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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)
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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) For a provisioned VN request , we define the
gross income GI() as:
The cost C() of provisioning a VN request :
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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:
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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
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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
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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)
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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
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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
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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
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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
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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 pm (from 0.001 to 0.1)
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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.
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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
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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)
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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
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國立清華大學高速通訊與計算實驗室NTHU High-Speed Communication & Computing Laboratory
Simulations
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國立清華大學高速通訊與計算實驗室NTHU High-Speed Communication & Computing Laboratory
Simulations
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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
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The End