 intended usage scenarios. The robust- ness of a RAN slice is essential throughout its lifecycle. In this chapter, we have formulated and solved two problems, i.e. a non-robust (i.e. the baseline) and a robust problem, to address the failures of SNs and the uncertainties of traffic demands in a RAN slice. Further- more, we developed a heuristic algorithm to tackle the robust problem, which produces the near-optimal solution. Numerical results have revealed that by solving the proposed robust RAN slicing algorithms, we can provide adjustable tolerance of link uncertainties. In this chapter, we assume that network functions are dedicated to one RAN slice. In the next stage, we will consider how to ensure isolation of RAN slices when some of the net- work functions are shared by multiple RAN slices. Moreover, we can combine RAN slicing with machine learning to develop an automatic RAN slicing model, since the monitoring data collected from the softwarized functions can provide effective features to refine slices' management decisions in return. References 1 del Río, J. and Gandía, D. (2016). D1.1 Refined Scenarios and Requirements, Consol- idated Use Cases, and Qualitative Techno-Economic Feasibility Assessment, Europe, METIS. Technical Report No. D1.1. 2 An, X., Zhou, C., Trivisonno, R. et al. (2016). On end to end network slicing for 5G communication systems. Transactions on Emerging Telecommunications Technologies 28: 1-13. 3 IMT-2020 (2016). White Paper on 5G Network Architecture Design, China, IMT-2020. Technical Report No. 2016-05. 4 NGMN Alliance (2015). 5G White Paper. Next-Generation Mobile Networks, White Paper, pp. 1-125. 5 NGMN (2016). Description of Network Slicing Concept by NGMN Alliance, Frankfurt, Germany, NGMN P1 WS1 E2E Architecture Team. Technical Report No. NGMN 5G P1. 6 Pentikousis, K., Wang, Y., and Hu, W. (2013). Mobileflow: toward software-defined mobile networks. IEEE Communications Magazine 51 (7): 44-53. 7 Mijumbi, R., Serrat, J., Gorricho, J.-L. et al. (2016). Network function virtualiza