Optimization-based Offloading and Resource Allocation Strategies for Vehicles in Highway Mobile Cloud Computing Systems

• Main Authors: Hsin-Yi Kuo, 郭欣宜
• Other Authors: 林永松
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/f23uxu

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Self-driving vehicle is an emerging technology which request many different types of tasks, including low-latency computation tasks and resource intensive computation tasks. Due to the limited computational capabilities and storage capacity of vehicles, serving such a large number of tasks has become a serious challenge in the vehicular network. Therefore, this study will use mobile cloud computing systems to overcome the problem of the limited resources in vehicles. By taking the advantages of the fixed route of highway, the direction and the speed of vehicles can be more predictable. In this thesis, we focus on using resource allocation strategy and offloading strategy better serve vehicle tasks in the cloud environment on the highway. We formulate the problem as a linear integer programming problem, in which the objective is to maximize the revenue of the cloud service provider. An algorithm based on the Lagrangian relaxation method and the subgradient method is used to solve this problem. A series of experiments are designed to test the performance of the algorithm. The experimental results show that the algorithm can have better and more stable feasible solutions under various network scenarios.