AI-Based E2E Resilient and Proactive Resource Management in Slice-Enabled 6G Networks

Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in network slicing (NetS) technology. This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS. Since guaranteeing quality of service (QoS) in NetS has many challenges, we present an intelligent method that has two characteristics: resilience and proactivity. Guaranteeing successful slice provision is costly, we formulate a comprehensive model of the imposed costs. To minimize the cost function, we introduce a new optimization problem with radio, processing, and transmission resource constraints. In addition, we introduce two new constraints that guarantee the proactivity and resilience capabilities of the network based on the probability of successful slice provisioning (PSSP). Since the proposed optimization problem is non-convex, online and belongs to the NP-hard category, we adopt a deep reinforcement learning (DRL) based method to solve it. In particular, the soft actor critic (SAC) method is utilized due to its robustness in uncertain environment that the obtained results reveal that the applied method can improve the percentage of successful slice provisioned (PrSSP). In addition, the resiliency time is reduced comparatively. Finally, as the main achievement, the resilient scenario improves PrSSP compared to the non-resilient scenario.