计算机科学
分布式计算
计算卸载
强化学习
服务器
异步通信
移动边缘计算
调度(生产过程)
上传
边缘计算
计算机网络
云计算
人工智能
数学优化
数学
操作系统
作者
Penglin Dai,Kai‐Wen Hu,Xiao Wu,Huanlai Xing,Zhaofei Yu
标识
DOI:10.1109/infocom42981.2021.9488886
摘要
Mobile edge computing (MEC) has been an effective paradigm to support real-time computation-intensive vehicular applications. However, due to highly dynamic vehicular topology, these existing centralized-based or distributed-based scheduling algorithms requiring high communication overhead, are not suitable for task offloading in vehicular networks. Therefore, we investigate a novel service scenario of MEC-based vehicular crowdsourcing, where each MEC server is an independent agent and responsible for making scheduling of processing traffic data sensed by crowdsourcing vehicles. On this basis, we formulate a data-driven task offloading problem by jointly optimizing offloading decision and bandwidth/computation resource allocation, and renting cost of heterogeneous servers, such as powerful vehicles, MEC servers and cloud, which is a mixed-integer programming problem and NP-hard. To reduce high time-complexity, we propose the solution in two stages. First, we design an asynchronous deep Q-learning to determine offloading decision, which achieves fast convergence by training the local DQN model at each agent in parallel and uploading for global model update asynchronously. Second, we decompose the remaining resource allocation problem into several independent subproblems and derive optimal analytic formula based on convex theory. Lastly, we build a simulation model and conduct comprehensive simulation, which demonstrates the superiority of the proposed algorithm.
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