| 标题 |
 Cooperative Task Offloading for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning 基于多Agent深度强化学习的移动边缘计算协同任务卸载
相关领域
计算机科学
服务器
强化学习
移动边缘计算
分布式计算
边缘计算
任务（项目管理）
架空（工程）
超时
GSM演进的增强数据速率
马尔可夫决策过程
地铁列车时刻表
边缘设备
计算机网络
人工智能
云计算
操作系统
管理
经济
统计
数学
马尔可夫过程
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| 网址 | |
| DOI |
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| 其它 | Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. |
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