Evaluation of Implementing Machine Learning Methods on Multi-Access Edge Computing Based Networks, Focusing on Computation Offloading, M.Sc. Thesis Sharif University of Technology ; Hajsadeghi, Khosrow (Supervisor)
Abstract
Many applications that require heavy processing, such as augmented reality, facial recognition, self-driving cars, and digital healthcare systems, are emerging. Many devices in the Internet of Things space cannot process such a volume of calculations. One of the newest methods to solve this problem is using new architectures in network design, among which we can refer to the edge computing architecture. In this method, by offloading the computation on the available computing resources close to the network's end nodes, we try to provide the results of the calculations for the end nodes of the network as quickly as possible. In this thesis, the problem of determining the computation offloading...
Cataloging briefEvaluation of Implementing Machine Learning Methods on Multi-Access Edge Computing Based Networks, Focusing on Computation Offloading, M.Sc. Thesis Sharif University of Technology ; Hajsadeghi, Khosrow (Supervisor)
Abstract
Many applications that require heavy processing, such as augmented reality, facial recognition, self-driving cars, and digital healthcare systems, are emerging. Many devices in the Internet of Things space cannot process such a volume of calculations. One of the newest methods to solve this problem is using new architectures in network design, among which we can refer to the edge computing architecture. In this method, by offloading the computation on the available computing resources close to the network's end nodes, we try to provide the results of the calculations for the end nodes of the network as quickly as possible. In this thesis, the problem of determining the computation offloading...
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