LAUSR

Network slicing in a 6G environment is an important research area in the current years. However, satisfying the demands of network slice requests is a challenging task. Energy-efficient, secure, and Quality of Service (QoS) aware network slicing is important since network slices must share fewer amounts of resources. Further, implementing secure network slicing for software-defined networks (SDNs)/network function virtualization (NFV) is crucial. In this article, we tackle the issues, such as security, QoS, and resource consumption issues through network slicing and load balancing mechanisms in SDN/NFV assisted 6G environments. First, deep network slicing is implemented using generative adversarial network (GAN) for network slicing and management. Based on the slice capacity, slice priority, and QoS demands of network slices, GAN predicts the appropriate slice and links for data transmission. For each slice, the directed acyclic graph (DAG)-based blockchain technology is used in which traditional consensus is replaced by the Proof of Space (PoS) algorithm. A limitation of scalability and high resource consumption in the traditional blockchain is addressed in DAG-blockchain. To improve security, context-based authentication, and secure handover schemes are presented using the Markov decision making (MDM) and weighted product model, respectively. Then, higher load faced at the SDN controllers and switches are addressed by intruder packets classification and packets migration through hybrid neural decision tree (HyDNT) and Hybrid Political optimizer with a Heap-based Optimizer (HPoHO), respectively. To predict the load accurately, environment learning is implemented using the soft actor-critic (SAC) algorithm. Finally, the performance of the proposed SliceBlock model is evaluated.