  One of the most important aspects of moving forward to the next generation
networks like 5G/6G, is to enable network slicing in an efficient manner. The
most challenging issues are the uncertainties in computation and communication
demand. Because the slices' arrive to the network in different times and their
lifespans vary, the solution should dynamically react to online slice requests.
The joint problem of online admission control and resource allocation
considering the energy consumption is formulated mathematically. It is based on
Binary Linear Programming (BLP), where, the $\Gamma$-Robustness concept is
exploited to overcome Virtual Links (VL) bandwidths' and Virtual Network
Functions (VNF) workloads' uncertainties. Then, an optimal algorithm is
proposed. This optimal algorithm cannot be solved in a reasonable amount of
time for real-world and large-scale networks. To find near-optimal solution
efficiently, a new heuristic algorithm is developed. The assessments' results
indicate that the efficiency of heuristic is vital in increasing the accepted
requests' count, decreasing power consumption and providing adjustable
tolerance vs. the VNFs workloads' and VLs traffics' uncertainties, separately.
Considering the acceptance ratio and power consumption that constitute the two
important components of the objective function, heuristic has about 7% and 10%
optimality gaps, respectively, while being about 30X faster than that of
optimal algorithm.
