e the assumption that the link resources are deterministic parameters. The aforementioned observations inspire us to investigate effective robust RAN slicing mechanisms to address the RAN slice recovery issue against link and network function fail- ures and the reconfiguration issue against the uncertain traffic demands in a unified frame- work. In this chapter, our contributions are three-fold. We first formulate a recovery prob- lem as a baseline, to solve remapping of a slice with deterministic demands, where remap- ping is a process of re-selecting VNFs and links to accommodate the failed ones. Based on that, we propose two robust RAN slicing algorithms for slice recovery and reconfiguration under stochastic demands: the first algorithm is an optimal one with slow convergence, which is developed by leveraging the robust optimization theory by transforming the prob- lem into its computational tractable equivalent, which is called robust failure recovery prob- lem (ROBUST) [18]. ROBUST can recover the failed RAN slice and also tackle the uncertain traffic demands by introducing redundancies. However, ROBUST is a mixed integer prob- lem (MIP) that is proved to be NP-hard [19] and convergence is very slow using the existing solver to obtain an optimal solution [20]. Nevertheless, the result of ROBUST can be used as the performance upperbound for evaluating other solutions. To address these problems, we propose a heuristic robust RAN slicing algorithm based on variable neighborhood search (VNS). The remainder of this chapter is organized as follows. Section 10.2 elaborates the ter- minology of RAN slicing and system model. In Section 10.3, we first formulate and solve the non-robust RAN slicing problem. Then, we propose two robust RAN slicing algorithms: the exact algorithm, i.e. ROBUST, and the heuristic algorithm, i.e. VNS. In Section 10.4, we dis- cuss the simulation results to evaluate the performance of the proposed robust RAN slicing algorithms. We draw a conclusion and point out some future work in Section 