车载云计算（VCC）是利用车辆上的计算和存储资源来提供计算服务的新技术转变。备用车载资源由VCC运营商（例如路边单元）集中，以使用车辆即资源框架完成任务请求。在本文中，我们研究及时服务供应的最后期限限制的任务，在VCC系统中，利用任务复制技术（即，允许一个任务由几个服务器车辆执行）。针对VCC系统中车辆运动的不确定性、车辆成员的不稳定性和车辆数量的大规模性等问题，提出了一种基于学习的车辆云任务调度算法DATE-V（Deadline-Aware Task rEplication for Vehicular Cloud）.该算法是基于一种新的上下文组合多臂强盗（CC-MAB）学习框架。DATE-V是“上下文”，因为它利用侧信息（上下文）的车辆和任务来推断随机车辆运动下的任务复制的完成概率。DATE-V是“组合的”，因为它的目的是复制接收到的任务，并将任务副本发送到多个服务器车辆，以保证服务的及时性。我们严格证明，我们的学习算法实现了次线性遗憾界相比，甲骨文算法，知道任何任务复制的确切完成概率。基于真实车辆运动轨迹进行了仿真，结果表明DATE-V的性能明显优于基准解决方案。
英文摘要：
Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a roadside unit, to complete task requests using the vehicle-as-a-resource framework. In this paper, we investigate timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by several server vehicles). A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel Contextual-Combinatorial Multi-Armed Bandit (CC-MAB) learning framework. DATE-V is `contextual' because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is `combinatorial' because it aims to replicate the received task and send the task replications to multiple server vehicles to guarantee the service timeliness. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.
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School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；University of Kent（肯特大学）；Hong Kong University of Science and Technology（香港科技大学）
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系，北京信息科学与技术国家研究中心）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信工程学院）；Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology (HKUST)（香港科学与技术大学电气与计算机工程系）
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、北京信息科学与技术国家研究中心）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信工程学院）；Qualcomm（高通）；School of Engineering, University of Kent（肯特大学工程学院）
