Energy And Traffic Aware Workload Offloading On Mobile Edge Computing In 5g Networks

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Ghiassi, Amir Masoud | 2018

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 51139 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Goudarzi, Maziar
Abstract:
With the emergence of 5G networks, response time is becoming increasingly more important. 5G networks facilitate usage of Mobile Edge Computing. MEC provides computing capabilities at the edges of cellular networks. Since the computational capability in mobile devices is limited, running high performance applications using external resources is a way to overcome this limitation. Workload offloading in MEC is an approach that provides additional computation capability for users to meet the desired response time. In this study, we presented a Mixed Integer Non-Linear Programing model for offloading different workloads on a heterogeneous set of MEC servers to minimize the SLA violations. We solved this model to find the optimal solution. We also proposed two heuristic algorithms to find a near optimal solution in a reasonable time. Finally, we performed several experiments to evaluate our proposed algorithm. The results show that our algorithm solution is at most 11% (8% on average) far from the optimal one
Keywords:
Optimization ; Migration ; Offloading ; Cloud Computing ; Service Level Agreement ; 5th Generation Cellular Network ; Work Load ; Mobile Edge Computing

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