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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The cloud computing category

Private clouds Public clouds

Mixed/Hybrid clouds

Bridge

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Scheduling model M/M/1 M/M/S

(7)

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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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System simulation

Scheduling host VM simulation of multiple host

Figure 3. Systematic structures

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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

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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.

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