Title page for etd-0627115-122915


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URN etd-0627115-122915
Author Tzu-Hua Huang
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2014
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title The study of virtual machine deployment in cloud computing platform
Date of Defense 2015-07-17
Page Count 93
Keyword
  • Cloud Computing
  • Docker
  • Swarm
  • PaaS
  • Bin-Packing
  • Real-Time Service
  • Abstract Nowadays cloud-computing is the hottest topics in the study of Internet. Through internet connection, it provided calculation service and allowed both software/hardware resources sharing and virtualization resource-management. This service has provided brand-new service style and irreplaceable advantages.
    There are three types of cloud-computing service model: SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service). Based on PaaS and researches of traditional virtual machine, the essay mainly discussed about issues of resource deployment and further designed a scheduling system with priority scheduling.
    Practically, Docker Container and Swarm Cluster Management system are used as the technique basis and implementation of this essay are based on Distributed Docker Management System (DDMS). The aim of the essay is to improve its way for resource deployment: maximum the service numbers and conclude real-time factor in scheduling system. The new design is to authorize enough priority and hardware resources to the urgent request, and also satisfied the need of basic calculation service. 
    In the essay, we used Bin-Packing algorithm to solve resource-deployment problems. We not only discussed about different Bin-Packing algorithm but also provided the refinement method to equip with Bulk Arrivals functionality while at the same time achieving approximate efficiency of Online Bin-Packing and similar result of Offline Bin-Packing. 
    Finally, trying to testify each algorithm’s impact on cloud service system’s performance, we used queuing system to compared four different algorithms’ impact in our model and analyze each reference data and correlations to provide a scheduling system which aligned with the need of Cluster Deployment from developer’s viewpoint.
    Advisory Committee
  • Wei-Kuang Lai - chair
  • Chien-Hsing Liu - co-chair
  • Hsiao-Guang Wu - co-chair
  • Cheng-Fou Chou - co-chair
  • Richard Lin - advisor
  • Files
  • etd-0627115-122915.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2015-07-29

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