Farreras Miquel, Paillissé Jordi, Fàbrega Lluís, Vilà Pere
Institute of Informatics and Applications, Universitat de Girona, C/ de la Universitat de Girona, 6, 17003 Girona, Spain.
UPC-BarcelonaTech, Carrer de Jordi Girona, 31, 08034 Barcelona, Spain.
Data Brief. 2024 Jul 15;55:110738. doi: 10.1016/j.dib.2024.110738. eCollection 2024 Aug.
This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the Radio Access Network (RAN) infrastructure. Each sample consists of pairs of a network scenario and the associated performance metrics: the network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of scenarios in multiple situations of over and underprovisioning. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.
本文介绍了一个全面的网络切片数据集，旨在为5G及以后（B5G）网络中的人工智能（AI）和基于数据的性能预测应用提供支持。该数据集通过分组级模拟器生成，考虑了3GPP定义的三种主要网络切片类型：增强型移动宽带（eMBB）、超可靠低延迟通信（URLLC）和大规模物联网（mIoT），捕捉了网络切片的复杂性。它包括具有不同拓扑、切片实例和流量流的广泛网络场景。所包含的场景包括传输网络，不包括无线接入网络（RAN）基础设施。每个样本由一个网络场景和相关性能指标对组成：网络配置包括网络拓扑、流量特征、路由配置，而性能指标是每个流的延迟、抖动和丢包。该数据集是通过一个定制的网络切片准入控制模块生成的，能够模拟资源过度分配和分配不足等多种情况下的场景。这个网络切片数据集是研究社区的宝贵资产，为5G和B5G网络的创新带来了机遇。
