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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Reinforcement learning based autonomic virtual machine management in clouds

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    12101056.pdf (1.160Mb)
    Date
    2016-04
    Publisher
    BRAC University
    Author
    Habib, Md. Arafat
    Metadata
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    URI
    http://hdl.handle.net/10361/5311
    Abstract
    Cloud computing is a rapidly emerging field, services and applications are more or less 24/7. Resource dimensioning in this field is a great issue. Research is already going on to imply reinforcement learning to automate decision making process in case of addition, reduction, migration and maintenance of the Virtual Machines (VM) to balance the service level performance and VM management cost. Models have been proposed in this case based on Q learning, a very popular reinforcement learning technique that is used to find optimal action selection policy for any finite Markov Decision Process (MDP).In this thesis, we propose to work with the challenges like proper initialization of the early stages, designing the states, actions, transitions using Markov Decision Process (MDP) and solving the MDP with two popular reinforcement learning techniques, Q learning and SARSA (λ).
    Keywords
    CSE; Computer science and engineering; Virtual machine management
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 43-44).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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