Efficient Task Offloading for 802.11p-Based Cloud-Aware Mobile Fog Computing System in Vehicular Networks
Efficient Task Offloading for 802.11p-Based Cloud-Aware Mobile Fog Computing System in Vehicular Networks
复制标题
车载网络中基于 802.11p 的云感知移动雾计算系统的高效任务卸载
DOI：
10.1155/2020/8816090
复制
发表时间：
2020-09
影响因子：
--
通讯作者：
Wu Guilu
中科院分区：
文献类型：
--
作者：
Wu Qiong;Ge Hongmei;Fan Qiang;Yin Wei;Chang Bo;Wu Guilu
各种新兴的车辆应用，如自动驾驶和安全预警，用于提高交通安全性和确保乘客舒适性。这些应用程序的完成需要大量的计算资源来执行巨大的延迟敏感/非延迟敏感和计算密集型任务。由于车载计算机的计算能力有限，很难满足这些应用的计算要求。为了解决这个问题，许多工作已经提出了一些有效的任务卸载计划，如移动的雾计算（MFC）的车辆网络的计算范例。在MFC中，车辆采用IEEE 802.11p协议来传输任务。根据IEEE 802.11p标准，任务可以根据延迟要求分为高优先级和低优先级。然而，没有现有的任务卸载工作考虑到由IEEE 802.11p的不同接入类别（AC）发送的任务的不同优先级。在本文中，我们提出了一个有效的任务卸载策略，以最大限度地提高长期预期的系统回报，在减少任务的执行时间。具体而言，我们共同考虑的影响，由不同的AC，车辆的移动性，和到达/离开的计算任务的任务的优先级，然后将卸载问题转化为一个半马尔可夫决策过程（SMDP）模型。然后，采用相对值迭代算法求解SMDP模型，寻找最优的任务卸载策略。最后，我们通过大量的实验来评估所提出的方案的性能。数值结果表明，该卸载策略相比贪婪算法表现良好。
Various emerging vehicular applications such as autonomous driving and safety early warning are used to improve the traffic safety and ensure passenger comfort. The completion of these applications necessitates significant computational resources to perform enormous latency-sensitive/nonlatency-sensitive and computation-intensive tasks. It is hard for vehicles to satisfy the computation requirements of these applications due to the limit computational capability of the on-board computer. To solve the problem, many works have proposed some efficient task offloading schemes in computing paradigms such as mobile fog computing (MFC) for the vehicular network. In the MFC, vehicles adopt the IEEE 802.11p protocol to transmit tasks. According to the IEEE 802.11p, tasks can be divided into high priority and low priority according to the delay requirements. However, no existing task offloading work takes into account the different priorities of tasks transmitted by different access categories (ACs) of IEEE 802.11p. In this paper, we propose an efficient task offloading strategy to maximize the long-term expected system reward in terms of reducing the executing time of tasks. Specifically, we jointly consider the impact of priorities of tasks transmitted by different ACs, mobility of vehicles, and the arrival/departure of computing tasks, and then transform the offloading problem into a semi-Markov decision process (SMDP) model. Afterwards, we adopt the relative value iterative algorithm to solve the SMDP model to find the optimal task offloading strategy. Finally, we evaluate the performance of the proposed scheme by extensive experiments. Numerical results indicate that the proposed offloading strategy performs well compared to the greedy algorithm.
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DOI：
10.1186/s13638-018-1025-5
发表时间：
2018-01
影响因子：
2.6
作者：
Qiong Wu;H. Zhang;Zhengquan Li;Yang Liu;Cui Zhang
通讯作者：
Qiong Wu;H. Zhang;Zhengquan Li;Yang Liu;Cui Zhang
影响因子：
4.1
作者：
Jindou Xie;Yunjian Jia;Zhengchuan Chen;Zhaojun Nan;Liang Liang-Liang
通讯作者：
Jindou Xie;Yunjian Jia;Zhengchuan Chen;Zhaojun Nan;Liang Liang-Liang
影响因子：
6.8
作者：
Hafeez, Khalid Abdel;Anpalagan, Alagan;Zhao, Lian
通讯作者：
Zhao, Lian
DOI：
10.1109/glocom.2014.7036784
发表时间：
2014-12
期刊：
2014 IEEE Global Communications Conference
影响因子：
--
作者：
Qiong Wu;Jun Zheng
通讯作者：
Qiong Wu;Jun Zheng
VANET IEEE 802.11p EDCA 机制的性能建模和分析
影响因子：
6.8
作者：
Jun Zheng;Qiong Wu
通讯作者：
Qiong Wu