• Login
    View Item 
    •   Mak UD Home
    • College of Engineering, Design, Art and Technology (CEDAT)
    • School of Engineering (SEng.)
    • School of Engineering (SEng.) Collections
    • View Item
    •   Mak UD Home
    • College of Engineering, Design, Art and Technology (CEDAT)
    • School of Engineering (SEng.)
    • School of Engineering (SEng.) Collections
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A machine learning based approach for multi-domain service deployment in future networks

    Thumbnail
    View/Open
    Undergraduate dissertation (1.771Mb)
    Date
    2020
    Author
    Fisehaye, Betelehem
    Metadata
    Show full item record
    Abstract
    The exponential increase of data traffic from new and improved services in healthcare, enhanced mobile broadband communication, transportation, augumented reality has led to cloudifing these services into an ordered set of virtual network functions known as Service Function Chains (SFC). These Service Function Chains(SFCs) ,deployed on remote cloud networks, are to be embedded onto the multiple domains belonging to different Infrastructure Providers(InPs) based on their resource requirements in relation to the available resources within the Infrastructure Providers (InPs) capacity while adhering to the stringent requirements of these services. The challenge lies when the Infrastructure Providers (InPs) are unwilling to disclose their internal topological information for security reasons which makes the effective allocation of resources complex. This calls for an intelligent algorithm that uses minimum information disclosed from the Infrastructure Providers (InPs) based on historical data to identify suitable domains for deployment. In this Project Work, a framework that deploys these Service Function Chains (SFCs) onto different domains that meet the delay constraints while at the same time respecting the privacy of Infrastructure Providers (InPs) is formulated. The partitioning of these Service Function Chain requests (SFC) is done by the proposed Reinforcement Learning Algorithm that identifies the most suitable candidate for deployment of the sub-SFCs. Simulation results, considering both online scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, an enhancement of the algorithm results in up to 5% improvement in terms of provisioning cost.
    URI
    http://hdl.handle.net/20.500.12281/10442
    Collections
    • School of Engineering (SEng.) Collections

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of Mak UDCommunities & CollectionsTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy TypeThis CollectionTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV