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首页> 外文期刊>Future generation computer systems >CODE-V: Multi-hop computation offloading in Vehicular Fog Computing
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CODE-V: Multi-hop computation offloading in Vehicular Fog Computing

机译：Code-V：车辆雾计算中的多跳计算卸载

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摘要

Vehicular Fog Computing (VFC) is an extension of fog computing in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency-aware and energy-aware application deployment in ITS. In this paper, we consider the problem of multi-hop computation offloading in a VFC network, where the client vehicles are connected to fog computing nodes by multi-hop LTE access points. Our scheme addresses three key aspects in a VFC architecture namely: (ⅰ) Optimal decision on local or remote task execution, (ⅱ) Optimal fog node assignment, and (ⅲ) Optimal path (multi-hop) selection for computation offloading. Considering the constraints on service latency, hop-limit, and computing capacity, the process of workload allocation across host vehicles, stationary and mobile fog nodes, and the cloud servers is formulated into a multi-objective, non-convex, and NP-hard Quadratic Integer Problem (QJP). Accordingly, an algorithm named Computation Offloading with Differential Evolution in VFC (CODE-V) is proposed. For each client task, CODE-V takes into account inter-fog cooperation, fog node acceptance probability, and the topological variations in the transportation fleets, towards optimal selection of a target fog node. We conduct extensive simulations on the real-world mobility traces of Shenzhen, China, to show that CODE-V reduces the average service latency and energy consumption by approximately 28% and 61%, respectively, compared to the state-of-the-art. Moreover, the CODE-V also gives better solution quality compared to standard DE/rand/1/bin algorithm and the solutions generated by a CPLEX solver.
机译：车辆雾计算（VFC）是智能交通系统（其）中雾计算的延伸。它是一个新兴计算模型，利用延迟感知和能量感知应用程序部署。在本文中，我们考虑VFC网络中的多跳计算卸载问题，其中客户端车辆通过多跳LTE接入点连接到雾计算节点。我们的计划在VFC架构中解决了三个关键方面，即：（Ⅰ）关于本地或远程任务执行的最佳决策，（Ⅱ）最佳雾节点分配，（Ⅲ）计算卸载的最佳路径（多跳）选择。考虑到服务延迟，跳跃限制和计算能力的约束，跨主机，静止和移动雾节点的工作负载分配过程，以及云服务器被配制到多目标，非凸和NP-HARD中二次整数问题（QJP）。因此，提出了一种以VFC（CODE-V）中的差分演进命名为计算卸载的算法。对于每个客户端任务，Code-V考虑了雾间合作，FOG节点接受概率和运输车队的拓扑变化，朝着目标雾节点的最佳选择。我们对中国深圳的现实世界行动迹线进行了广泛的模拟，以证明该码-V与最先进的情况相比，Code-V分别将平均服务延迟和能源消耗降低了约28％和61％ 。此外，与标准DE / RAND / 1 / BIN算法和由CPLEX求解器产生的解决方案相比，COD-V还提供了更好的解决方案质量。

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