排隊理論_An Exploration of The Optimization of Executive Scheduling in The Cloud Computing
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Transcript of 排隊理論_An Exploration of The Optimization of Executive Scheduling in The Cloud Computing
An Exploration of The Optimization of Executive Scheduling in The Cloud Computing
Chih-yung chen, Hsiang-yi tseng
資訊工程學系F74986159 蔡婉萍
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Outline Introduction(Research motivation & Purpose)
The Cloud computing architecture
The cloud computing category
Scheduling model
System development process
System structure
Model set
System simulation
Conclusion
Comment
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Introduction• Research motivation • The cloud computing has become the focus IT industry, the use of the cloud
computing can reduce wastage of resources and efficient upgrade effectiveness . It also can import working scheduling model for best use rate of hosts.
• Research Purpose• Explore the difference of the working scheduling in the cloud computing.• Explore the working scheduling applications in the cloud computing.
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The Cloud computing architecture
SaaS (Software as a Service)
PaaS (Platform as a Service)
IaaS (Infrastructure as a Service)
Server Network Storage
Figure 1.Framework of cloud computing
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System development processDemand Analysis
Model Set
System design
System construction
Data analysis and compare
Figure 2. System development process chart
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Model set We selected M/M/1 and M/M/S the two models to compare and analysis.
First define the platform scheduling =10, 20, 30.
By means of the changes of single average scheduling execution time to observe the change of system service rates, Find the time to be suitable for increasing the service platform.
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Architecture features
User
Select scheduling implementation
modalities
M/M/1/FCFS/ Model
M/M/s/FCFS/ Model
<<uses>>
<<extends>>
<<extends>>
Figure 4. Systematic use case
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System service rate change
Scheduling average length of service Scheduling average length of service
Syst
em s
ervi
ce ra
tes
Syst
em s
ervi
ce ra
tes
Figure 5. M/M/1 system service rate change Figure 6. M/M/2 system service rate change
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Comparisons between M/M/1 and M/M/2
Scheduling average length of service
Syst
em s
ervi
ce ra
tes
Figure 7. Comparisons between M/M/1 and M/M/2 Figure 8. M/M/1 and M/M/2
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Conclusion In this article, we have the cloud computing and the queuing theory on the basis and the simulation of users in accordance with demand category of parameters. Scheduling the parameters can access to Internet usage or a singlet the time to do the parameters. Use the cloud computing through queuing theoretical models of the produced data that try to classify the best of the model, to provide an effective feasibility of proposals to help resolve the cloud computing user could provide a basis, and achieve more closely user's computer resource requirements.
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Comments This paper should compares the simulation with more cases. The parameter settings of demand category should be more close to the real situation.