| 移动边缘计算中的在线任务卸载方法 |
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| 作者姓名： | 刘婷 罗喜良 |
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| 作者单位： | 1.上海科技大学 信息科学与技术学院, 上海 201210;2.中国科学院上海微系统与信息技术研究所, 上海 200050;3.中国科学院大学, 北京 100049 |
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| 基金项目： | 国家自然科学基金面上项目（61971286）资助 |
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| 摘 要： | 为减少移动边缘计算(mobile edge computing,MEC)网络中移动用户的长期任务开销,利用强化学习的马尔科夫决策过程,将用户的移动性与系统的动态信息建模为随机优化问题.依据系统信息的状态,将问题分为系统信息已知、系统信息未知2种情况.在系统信息已知时,提供了问题的最优解;系统信息未知时,基于在线学习提出...
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| 关 键 词： | 移动边缘计算 任务卸载 在线学习 马尔科夫决策过程 |
| 收稿时间： | 2020-01-15 |
| 修稿时间： | 2020-05-05 |
Online task offloading in mobile edge computing |
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| Authors: | LIU Ting LUO Xiliang |
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| Institution: | 1.School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China;2.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;3.University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | To reduce the costs of task processing, task offloading is put forward as a promising technology in mobile edge computing. In this paper, in order to lessen the burden of long-term tasks of the moving user, we utilize Markov decision process to formulate the task offloading problem as a stochastic programming problem, when taking the user mobility and system dynamics into account. According to the system information state, the problem can be categorized as the one with fully known system information and the other one with limited system information. We provide the optimal and learning-based task offloading algorithms under these two kinds of systems respectively. Furthermore, two learning-based algorithms, one with optimality and another with faster convergence rate, are proposed. The performance is verified in simulations. |
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| Keywords: | mobile edge computing(MEC) task offloading online learning Markov decision process(MDP) |
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