This document summarizes research on optimizing executive scheduling in cloud computing. It outlines the cloud computing architecture and categories. It then discusses developing a scheduling model and system to simulate scheduling across multiple hosts using queuing theory models. The system is tested by varying service rates and scheduling parameters to analyze performance. It concludes the models can help classify the best scheduling approach to more closely match user computer resource needs. It suggests comparing the simulation to more cases and setting demand parameters closer to real situations.

1. 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

2. 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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3. 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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4. 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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5. The cloud computing category
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Private clouds Public clouds
Mixed/Hybrid
clouds
Bridge

6. Scheduling model
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7. System development process
Demand Analysis
Model Set
System design
System construction
Data analysis and compare
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Figure 2. System development process chart

8. Model set
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9. System simulation
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Scheduling host VM simulation of multiple host
Figure 3. Systematic structures

10. Architecture features
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User
Select scheduling
implementation
modalities
<<uses>>
<<extends>>
<<extends>>
Figure 4. Systematic use case

11. System service rate change
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Scheduling average length of service Scheduling average length of service
Systemservicerates
Systemservicerates
Figure 5. M/M/1 system service rate change Figure 6. M/M/2 system service rate change

12. Comparisons between M/M/1 and M/M/2
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Scheduling average length of service
Systemservicerates
Figure 7. Comparisons between M/M/1 and M/M/2 Figure 8. M/M/1 and M/M/2

13. 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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14. 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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