Mu Liting, Ge Bin, Xia Chenxing, Wu Cai
College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, China.
Entropy (Basel). 2022 Jun 11;24(6):814. doi: 10.3390/e24060814.
Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.
车载边缘计算是一种新的计算范式。通过将边缘计算引入车联网（IoV），服务提供商能够为用户提供低延迟服务，因为边缘计算在靠近车联网用户的一侧部署资源（例如计算、存储和带宽）。当移动节点移动并生成结构化任务时，它们可以与路边单元（RSU）连接，然后选择合适的时间和几个合适的移动边缘计算（MEC）服务器来卸载任务。然而，如何高效地按顺序卸载任务具有挑战性。针对这个问题，在本文中，我们提出了一种采用最优停止理论（OST）原理的时间优化多任务卸载模型，目标是最大化卸载到最优服务器的概率。当服务器利用率接近均匀分布时，我们提出了另一种基于OST的模型，目标是最小化总卸载延迟。使用模拟数据集和真实数据集，将所提出的模型与相关的OST模型进行了实验比较和评估，并进行了敏感性分析。结果表明，所提出的卸载模型能够在移动节点中高效实现，并显著减少任务的总预期处理时间。
