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Increasing the quality of services and resource utilization in vehicular cloud computing using best host selection methods
Cluster Computing ( IF 4.4 ) Pub Date : 2020-07-27 , DOI: 10.1007/s10586-020-03159-2
Reza Sookhtsaraei , Marzieh Iraji , Javad Artin , Mohammad Saber Iraji

One of the technology for increasing the safety and welfare of humans in roads is Vehicular Cloud Computing (VCC). This technology can utilize cloud computing advantages in the Vehicular Ad Hoc Network (VANET). VCC by utilizing modern equipment along with current vehicles, can play a significant role in the area of smart transportation systems. Given the potential of this technology, effective methods for managing existing resources and providing the expected service quality that is essential for such an environment are not yet available as it should. One of the most important barriers to providing such solutions seems to be resource constraints and very high dynamics in vehicles in VCC. In this article, based on virtualization and taking into account the environment with these features, we propose simple ways to manage resources better and improve the quality of service. We were able to achieve better results in simulation than previous methods by providing a flexible data structure to control the important data in the environment effectively. To illustrate the impact of the proposed methods, we compared them with some of the most important methods in this context, and we used SUMO 1.2.0 and MATLAB R2019a software to simulate them. Simulation results indicate that the proposed methods provide better results than previous methods in terms of resource efficiency, Quality of Service (QoS), and load balancing.



中文翻译:

使用最佳主机选择方法提高车辆云计算中的服务质量和资源利用率

车辆云计算(VCC)是提高道路上人员安全和福利的一项技术。该技术可以利用车载自组织网络(VANET)中的云计算优势。通过将现代设备与当前的车辆一起使用,VCC可以在智能交通系统领域中发挥重要作用。鉴于这项技术的潜力,目前尚无法提供有效的方法来管理现有资源并提供对于此类环境至关重要的预期服务质量。提供此类解决方案的最重要障碍之一似乎是资源限制和VCC中车辆的高动态。在本文中,基于虚拟化并考虑到具有这些功能的环境,我们提出了一些简单的方法来更好地管理资源并提高服务质量。通过提供灵活的数据结构来有效控制环境中的重要数据,我们能够在仿真方面取得比以前的方法更好的结果。为了说明所提出方法的影响,我们将它们与这种情况下的一些最重要方法进行了比较,并使用SUMO 1.2.0和MATLAB R2019a软件对其进行了仿真。仿真结果表明,所提出的方法在资源效率,服务质量(QoS)和负载平衡方面都比以前的方法提供了更好的结果。为了说明所提出方法的影响,我们将它们与这种情况下的一些最重要方法进行了比较,并使用SUMO 1.2.0和MATLAB R2019a软件对其进行了仿真。仿真结果表明,所提出的方法在资源效率,服务质量(QoS)和负载平衡方面都比以前的方法提供了更好的结果。为了说明所提出方法的影响,我们将它们与这种情况下的一些最重要方法进行了比较,并使用SUMO 1.2.0和MATLAB R2019a软件对其进行了仿真。仿真结果表明,所提出的方法在资源效率,服务质量(QoS)和负载平衡方面都比以前的方法提供了更好的结果。

更新日期:2020-07-27
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