排隊理論_An Exploration of The Optimization of Executive Scheduling in The Cloud Computing

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對於 雲端運算中 執行的排程 最佳化的探討

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排隊理論_An Exploration of The Optimization of Executive Scheduling in The Cloud Computing

  1. 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. 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 2
  3. 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. 3
  4. 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 4
  5. 5. The cloud computing category 5 Private clouds Public clouds Mixed/Hybrid clouds Bridge
  6. 6. Scheduling model 6
  7. 7. System development process Demand Analysis Model Set System design System construction Data analysis and compare 7 Figure 2. System development process chart
  8. 8. Model set 8
  9. 9. System simulation 9 Scheduling host VM simulation of multiple host Figure 3. Systematic structures
  10. 10. Architecture features 10 User Select scheduling implementation modalities <<uses>> <<extends>> <<extends>> Figure 4. Systematic use case
  11. 11. System service rate change 11 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. 12. Comparisons between M/M/1 and M/M/2 12 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. 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. 13
  14. 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. 14
  15. 15. 15 Thanks for Your Attention !

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