Download Optimal Provisioning for Elastic Service Oriented

Optimal Provisioning for
Elastic Service Oriented
Virtual Network Request in
Cloud Computing
101062558 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
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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
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Cloud computing paradigm enables users to
access services and applications hosted in
data centers based on their requirements.
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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
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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)
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For a provisioned VN request 𝐺𝑉 , we define the
gross income GI(𝐺𝑉 ) as:
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The cost C(𝐺𝑉 ) of provisioning a VN request 𝐺𝑉 :
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)
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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)


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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
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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
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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
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Cloning
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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
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Mutation
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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 (from 0.001 to 0.1)
m
國立清華大學高速通訊與計算實驗室
NTHU High-Speed Communication & Computing Laboratory
Chromosome Operations
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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
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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
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In this work we address the problem of optimal
provisioning for elastic service oriented VN request in
cloud-based datacenters.
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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
國立清華大學高速通訊與計算實驗室
NTHU High-Speed Communication & Computing Laboratory