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dc.contributor.supervisor Hossain, Ekram (Electrical and Computer Engineering) en_US
dc.contributor.author Ranadheera, Shermila
dc.date.accessioned 2018-01-10T15:55:47Z
dc.date.available 2018-01-10T15:55:47Z
dc.date.issued 2017
dc.identifier.uri http://hdl.handle.net/1993/32769
dc.description.abstract Fifth generation (5G) dense small cell networks are expected to meet the thousand-fold mobile traffic challenge within the next few years. Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as Mobile Edge Computing (MEC). One application of MEC is computation offloading, where users offload computationally expensive tasks to the edge nodes. Two main challenges of MEC are offloading decision making problem and MEC server resource allocation problem. While MEC servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, they suffer from limitations in computational and radio resources. This calls for efficient resource management in the MEC servers. This problem is challenging due to the ultra-high density, distributed nature, and intrinsic randomness of next generation wireless networks. Thus, when developing solution schemes, conventional centralized control may no longer be viable. Instead, distributed decision making mechanisms with low complexity would be desirable to make the network self-organizing and autonomous. Hence, it is imperative to develop distributed mechanisms for computation offloading, such that the users' latency constraints are fulfilled, while the computational servers are utilized at their best capacity. Game theory is well-established as a classic tool to mathematically model the wireless resource allocation problems and to develop distributed decision making schemes. Since game theory focuses on strategic interactions among players, it eliminates the need for a central controller which is a major advantage. In this thesis, I investigate two main challenges in computation offloading mentioned above: (i) computation offloading decision making and (ii) energy efficient activation of MEC servers. For both cases, I focus on the objective of achieving efficient resource allocation of MEC servers while meeting users' latency requirements. To this end, I develop distributed decision making schemes to solve these problems using the theory of minority games. I demonstrate the performance of the proposed methods using simulations. en_US
dc.subject Computation offloading, mobile edge networks, minority game en_US
dc.title Computation offloading in mobile edge networks: a minority game model en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Yahampath, Pradeepa (Electrical and Computer Engineering) Mohammed, Noman (Computer Science) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note February 2018 en_US


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