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An Approach for Dynamic Selection of Virtual Machines for Migration in Cloud Data Centers

Rezakhani, Mahshid | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53559 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Movaghar Rahimabadi, Ali
  7. Abstract:
  8. Increasing the workload of physical machines in data centers, performance of the system decreases and energy consumption increases. Various methods have been proposed to overcome this challenge and solve the problem. One of these solutions is the migration of virtual machines (VMs) from the over-loaded physical machine (PM) to another one, namely the under-loaded PM, in the clouds, which is effective in increasing the performance of the system and improving energy consumption When the performance of a PM is less than a certain threshold, due to overload, migration of the selected VM to a candidate PM can improve performance, if it is done in an intelligent way. Choosing the best suitable VM for migration, according to the resource usage and energy consumption of the VM, and considering the service level agreements (SLAs) documented between customers and providers, will have a great impact on the speed and rate of data transfer, the level of the performance degradation, and energy consumption of the system during the migration. This thesis presents a VM selection approach, based on dynamic reinforcement learning method, in order to increase the performance and reach an acceptable level of energy consumption by choosing a suitable VM for migration. The presented algorithm learns by following the set policies and decides which virtual machine is suitable for migration according to the current situation. Simulation results show that our proposed algorithm minimizes the energy consumption and SLA violation. We represent that the algorithm presented in this thesis is more efficient than the previously presented methods, in terms of performance and power savings
  9. Keywords:
  10. Data Center ; Energy Efficiency ; Machine Learning ; Efficiency ; Virtual Machine ; Live Migration ; Cloud Data Center

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