Generation of a network slicing dataset: the foundations for AI-based B5G resource management

This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network 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 RAN infrastructure. Each sample consists of pairs of (network scenario, 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 realistic scenarios without violating SLAs. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.