多域蜂窝网络中云边缘车辆通信的数据发生器

Marco Pomalo, V. T. Le, Nabil El Ioini, C. Pahl, H. Barzegar
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引用次数: 3

摘要

电信和蜂窝网络技术的快速发展催生了一系列十年前被认为是不可能的服务和场景。不同的架构、场景和用例可以部署在不同代的蜂窝网络之上,以解决不同的业务用例。某些场景由于其关键用途而需要高水平的可靠性,例如车辆边缘计算,医疗物联网等。当在网络边缘提供服务时,交换的信息需要是最新的和有效的,以便进行系统性能评估和建模。然而,为了进行实验,访问有效和可靠的电信数据(例如,eNodeB(基站)属性和配置)并不容易,因为在大多数情况下,数据要么是机密的,要么至少是难以获得的,特别是在处理跨组织边界时(例如,来自多个电信运营商的数据)。为了弥补这一差距,并允许研究人员使用电信数据构建、测试和分析新的协议和算法,我们为多域蜂窝网络设计了一个移动数据生成器(DG)。我们的生成器提供了一系列可能的配置,并处理包括多个参与者、机构和组织的场景。在本文中,我们介绍了我们的发电机的设计和实现。我们通过考虑不同的场景来评估数据生成器,具体来说,我们已经测试了服务中断和移动网络迁移,因为这些场景需要大量的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Data Generator for Cloud-Edge Vehicle Communication in Multi Domain Cellular Networks
The rapid development of telecommunications and cellular network technologies gave birth to a range of services and scenarios that were considered impossible a decade ago. Various architectures, scenarios, and use-cases can be deployed on top of the different generations of cellular networks to solve different business cases. Some scenarios require a high level of reliability due to their critical usage e.g., Vehicular Edge computing, medical IoT and so on. When offering services at the edge of the network, the information exchanged needs to be current and valid for systematic performance assessment and modeling. However, in order to run experiments, access to valid and reliable telecommunication data e.g., eNodeB (Base Station) properties, and configurations is not easy, since in most cases data is either confidential or at least difficult to obtain, especially when dealing with cross organizational boundaries (e.g., data coming from multiple telecom operators). To bridge this gap and allow researchers to build, test and analyze new protocols and algorithms with telecommunication data, we designed a mobile data generator (DG) for multi-domain cellular networks. Our generator provides a range of possible configurations and handles scenarios that include multiple participants, authorities and organizations. In this paper, we present the design and implementation of our generator. We evaluated the data generator by considering different scenarios, specifically, we have tested service interruptions and mobile network migration since these scenarios require a considerable amount of data.
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