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

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...

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Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Gholamipour, Sajjad, Akbari, Behzad, Mokari, Nader, Mohammad Mahdi Tajiki, Jorswieck, Eduard Axel
Format: Article
Language:English
Subjects:
Admission control
Algorithms
Energy consumption
Heuristic
Heuristic methods
Integer programming
Linear programming
Network slicing
Optimization
Power consumption
Resource allocation
Robustness (mathematics)
Uncertainty
Virtual networks
Workload
Workloads
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Summary: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.
ISSN:2331-8422