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One of the most important aspects of moving forward to the next generationnetworks like 5G/6G, is to enable network slicing in an efficient manner. Themost challenging issues are the uncertainties in computation and communicationdemand. Because the slices' arrive to the network in different times and theirlifespans vary, the solution should dynamically react to online slice requests.The joint problem of online admission control and resource allocationconsidering the energy consumption is formulated mathematically. It is based onBinary Linear Programming (BLP), where, the $\Gamma$-Robustness concept isexploited to overcome Virtual Links (VL) bandwidths' and Virtual NetworkFunctions (VNF) workloads' uncertainties. Then, an optimal algorithm isproposed. This optimal algorithm cannot be solved in a reasonable amount oftime for real-world and large-scale networks. To find near-optimal solutionefficiently, a new heuristic algorithm is developed. The assessments' resultsindicate that the efficiency of heuristic is vital in increasing the acceptedrequests' count, decreasing power consumption and providing adjustabletolerance vs. the VNFs workloads' and VLs traffics' uncertainties, separately.Considering the acceptance ratio and power consumption that constitute the twoimportant components of the objective function, heuristic has about 7% and 10%optimality gaps, respectively, while being about 30X faster than that ofoptimal algorithm.

Online Admission Control and Resource Allocation in Network Slicing under Demand Uncertainties